From 05078c00fad816cce810fef4c7b058437b289332 Mon Sep 17 00:00:00 2001 From: halvesrsq Date: Sat, 7 Nov 2026 22:20:42 +0300 Subject: [PATCH] =?UTF-8?q?academy/evren:=20curriculum=20v0.2=20=E2=80=94?= =?UTF-8?q?=20adaptive=20selection,=20retention,=20diagnosis?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - selection.py: LearningProgressSelector (pooled family signatures, |Δ success| learning progress, per-atom-class weakness targeting, novelty recovery; deterministic, checkpointable) - diversity.py: Jaccard/entropy/coverage/novelty metrics - retention.py: frozen-substrate repeated-probe protocol (per-config) - diagnosis.py: root-causes the v0.1 null result (eval artifact: one-config-per-seed draws; plus trained-state memory saturation) - curriculum_experiment.py: matched-budget 5-condition comparison (uniform/ascending/calibrated/proc_lp/teacher_lp), per-config eval grids, paired bootstrap CIs, zero API calls, state persistence - CLI: evren-curriculum, evren-diagnose (warn on hash randomization) - configs/academy_v01.toml: [evren.curriculum] section - tests: +5 suites (selection/diversity/retention/diagnosis/experiment) - docs/EVREN_CURRICULUM_V02.md: protocol + measured results - pinned run (PYTHONHASHSEED=0): LP and teacher-source effects exactly +0.0000 on classic transfer; ascending only non-uniform (+0.042); retention flat. Honest negative result: student memory saturation is the binding constraint, not curriculum source. --- README.md | 16 + academy/cli/main.py | 93 + academy/evren/curriculum_experiment.py | 584 ++ academy/evren/diagnosis.py | 210 + academy/evren/diversity.py | 162 + academy/evren/retention.py | 143 + academy/evren/selection.py | 403 + .../tests/test_evren_curriculum_experiment.py | 167 + academy/tests/test_evren_diagnosis.py | 67 + academy/tests/test_evren_diversity.py | 85 + academy/tests/test_evren_retention.py | 100 + academy/tests/test_evren_selection.py | 199 + academy/tools/report_curriculum.py | 56 + configs/academy_v01.toml | 28 +- docs/EVREN_CURRICULUM_V02.md | 328 + .../evren/curriculum_v02_summary.json | 8874 +++++++++++++++++ .../academy_v01/evren/diagnosis_summary.json | 742 ++ .../evren/policies_v02/ascending_agent.json | 5514 ++++++++++ .../evren/policies_v02/calibrated_agent.json | 4415 ++++++++ .../evren/policies_v02/proc_lp_agent.json | 5472 ++++++++++ .../evren/policies_v02/teacher_lp_agent.json | 7074 +++++++++++++ .../evren/policies_v02/uniform_agent.json | 7118 +++++++++++++ 22 files changed, 41848 insertions(+), 2 deletions(-) create mode 100644 academy/evren/curriculum_experiment.py create mode 100644 academy/evren/diagnosis.py create mode 100644 academy/evren/diversity.py create mode 100644 academy/evren/retention.py create mode 100644 academy/evren/selection.py create mode 100644 academy/tests/test_evren_curriculum_experiment.py create mode 100644 academy/tests/test_evren_diagnosis.py create mode 100644 academy/tests/test_evren_diversity.py create mode 100644 academy/tests/test_evren_retention.py create mode 100644 academy/tests/test_evren_selection.py create mode 100644 academy/tools/report_curriculum.py create mode 100644 docs/EVREN_CURRICULUM_V02.md create mode 100644 results/academy_v01/evren/curriculum_v02_summary.json create mode 100644 results/academy_v01/evren/diagnosis_summary.json create mode 100644 results/academy_v01/evren/policies_v02/ascending_agent.json create mode 100644 results/academy_v01/evren/policies_v02/calibrated_agent.json create mode 100644 results/academy_v01/evren/policies_v02/proc_lp_agent.json create mode 100644 results/academy_v01/evren/policies_v02/teacher_lp_agent.json create mode 100644 results/academy_v01/evren/policies_v02/uniform_agent.json diff --git a/README.md b/README.md index ef919e4..5e2a573 100644 --- a/README.md +++ b/README.md @@ -206,6 +206,22 @@ python -m academy.cli.main evren-pilot python -m academy.cli.main evren-status ``` +EVREN curriculum research v0.2 (offline by construction — zero API calls): + +```bash +# diagnosis of the v0.1 null result (uses saved policies when present) +python -m academy.cli.main evren-diagnose + +# matched-budget adaptive-curriculum comparison (5 conditions) +python -m academy.cli.main evren-curriculum + +# quick sanity run (2 conditions, 16 episodes) +python -m academy.cli.main evren-curriculum --quick +``` + +See `docs/EVREN_CURRICULUM_V02.md` for the scientific protocol and +measured results. + --- ## Research principles diff --git a/academy/cli/main.py b/academy/cli/main.py index eea27c0..8de7a3c 100644 --- a/academy/cli/main.py +++ b/academy/cli/main.py @@ -44,6 +44,8 @@ PRIMARY_TEACHER, SECONDARY_TEACHER, api_key_from_env, ) from academy.evren.task_registry import TaskRegistry # noqa: E402 +from academy.evren.curriculum_experiment import run_curriculum_experiment # noqa: E402 +from academy.evren.diagnosis import run_diagnosis # noqa: E402 def policy_factories(cfg): @@ -496,6 +498,82 @@ def cmd_evren_train(args, cfg): return 0 +def cmd_evren_curriculum(args, cfg): + """v0.2 matched-budget curriculum experiment (offline; zero API calls). + + For cross-process reproducibility this should run with + ``PYTHONHASHSEED=0`` (the student's state-dependent planning iterates + frozensets whose order is randomized otherwise). The summary records + the actual flag; a warning is printed when unpinned.""" + import sys as _sys + if _sys.flags.hash_randomization: + print("WARNING: PYTHONHASHSEED is randomized; rerun with " + "PYTHONHASHSEED=0 for cross-process reproducibility.") + splits = load_splits(cfg) + results_dir = registry_paths(cfg) + registry = TaskRegistry(results_dir, run_id="evren-exp") + e = evren_cfg(cfg).get("curriculum", {}) or {} + episodes = args.episodes or int(e.get("episodes", 160)) + conditions = (args.conditions.split(",") if args.conditions + else ["uniform", "ascending", "calibrated", "proc_lp", + "teacher_lp"]) + if args.quick: + episodes = min(episodes, 16) + conditions = list(conditions)[:2] + summary = run_curriculum_experiment( + cfg, splits, registry, results_dir=results_dir, + run_id="curriculum-v02", + conditions=tuple(conditions), + episodes=episodes, + block_size=args.block or max(8, episodes // 4), + verbose=True) + print("== EVREN curriculum v0.2 experiment (offline) ==") + for cond, d in summary["conditions_detail"].items(): + cls_ = d["classic_test"]["config_mean_success"] + ood = d["ood_probe"]["config_mean_success"] + ret = d["retention_classic"]["retention_delta"] + hold = d["teacher_holdout"] + hold_s = (f"{hold['config_mean_success']:.3f}" + if hold else "n/a") + print(f" {cond:11s} classic={cls_:.3f} ood={ood:.3f} " + f"teacher_hold={hold_s} retention={ret:+.3f}") + for name, cmp_ in summary.get("comparisons", {}).items(): + if cmp_: + lo, hi = cmp_["ci"] + print(f" {name}: mean={cmp_['mean']:+.4f} " + f"CI95=[{lo:+.4f},{hi:+.4f}]") + return 0 + + +def cmd_evren_diagnose(args, cfg): + """Root-cause diagnosis of the v0.1 null result (offline).""" + import sys as _sys + if _sys.flags.hash_randomization: + print("WARNING: PYTHONHASHSEED is randomized; rerun with " + "PYTHONHASHSEED=0 for cross-process reproducibility.") + splits = load_splits(cfg) + results_dir = registry_paths(cfg) + summary = run_diagnosis( + results_dir=results_dir, splits=splits, + n_configs=args.configs, episodes=args.episodes, + train_fallback=args.fallback_train) + a = summary["aggregate"] + print("== EVREN v0.1 null-result diagnosis ==") + fresh = a["fresh"] + print(f"fresh agents: success={fresh['success_rate']:.3f} " + f"contradicted_hyps={fresh['mean_contradicted']:.1f} " + f"verified_hyps={fresh['mean_verified']:.1f}") + for name in summary["trained_agents"]: + tr = a[name] + print(f"{name:14s} success={tr['success_rate']:.3f} " + f"delta_vs_fresh={tr['delta_vs_fresh']:+.3f} " + f"contradicted={tr['mean_contradicted']:.1f} " + f"verified={tr['mean_verified']:.1f} " + f"tried_entries={tr['mean_tried_entries']:.1f}") + print(f" written to {os.path.join(results_dir, 'diagnosis_summary.json')}") + return 0 + + def cmd_evren_status(args, cfg): results_dir = registry_paths(cfg) if not os.path.isdir(results_dir): @@ -557,6 +635,17 @@ def main(argv=None): p_et.add_argument("--eval-seeds", type=int, default=None) p_et.add_argument("--eval-episodes", type=int, default=None) sub.add_parser("evren-status") + p_ec = sub.add_parser("evren-curriculum") + p_ec.add_argument("--episodes", type=int, default=None) + p_ec.add_argument("--block", type=int, default=None) + p_ec.add_argument("--conditions", default=None) + p_ec.add_argument("--quick", action="store_true") + p_ed = sub.add_parser("evren-diagnose") + p_ed.add_argument("--configs", type=int, default=10) + p_ed.add_argument("--episodes", type=int, default=10) + p_ed.add_argument("--fallback-train", type=int, default=None, + help="train a quick reference agent if no saved " + "policy states are found") parser.add_argument("--override", action="append", default=None, help="flat override key=value, e.g. evaluation.seeds=8") @@ -600,6 +689,10 @@ def main(argv=None): return cmd_evren_offline(args, cfg) if args.command == "evren-train": return cmd_evren_train(args, cfg) + if args.command == "evren-curriculum": + return cmd_evren_curriculum(args, cfg) + if args.command == "evren-diagnose": + return cmd_evren_diagnose(args, cfg) if args.command == "evren-status": return cmd_evren_status(args, cfg) parser.print_help() diff --git a/academy/evren/curriculum_experiment.py b/academy/evren/curriculum_experiment.py new file mode 100644 index 0000000..c7a1238 --- /dev/null +++ b/academy/evren/curriculum_experiment.py @@ -0,0 +1,584 @@ +"""EVREN curriculum v0.2 — matched-budget curriculum comparison. + +Scientific design (corrects three confounds of the v0.1 experiment): + +1. **Selection was order-blind.** v0.1 dropped teacher tasks into a + fixed stream at p=0.5. Here the adaptive conditions (``proc_lp``, + ``teacher_lp``) choose each treatment episode with learning-progress + + weakness scores built from the student's own outcomes + (``selection.py``). + +2. **Retention was unmeasurable.** v0.1 probed forgetting once, after + training, with a probe identical across conditions. Here every + condition trains on the *same frozen substrate* (classic configs + + the same teacher tasks) at a matched slot share, and the substrate is + re-probed at every block boundary — a retention *curve* per + condition. + +3. **Exposure mix was entangled.** v0.1's teacher condition saw half as + many classic episodes. Here all conditions share the substrate slot + share and differ only in what the remaining treatment slots contain, + so each comparison isolates one factor: + + uniform → uniform draws from the treatment pool (status quo) + ascending → deterministic easy→hard ordering (procedural baseline) + calibrated → the Academy's measured-progress scheduler (procedural) + proc_lp → learning-progress + weakness selection over the pool + teacher_lp → same machinery, treatment pool augmented with + teacher-approved tasks (source ablation) + + principled pairwise deltas: lp effect = proc_lp − uniform; + teacher-source effect = teacher_lp − proc_lp. + +**Budgets are matched exactly**: every condition consumes the same +number of student training episodes and the same substrate exposure. +The ``calibrated`` condition additionally runs a scratch-agent +difficulty calibration (throwaway episodes that never touch the +condition's trained student); that measurement cost is recorded +separately and disclosed. + +**Evaluation is frozen and shared**: classic test pool (Evaluator, +isolation-checked), held-out teacher tasks, and a small OOD probe use +identical matched streams across conditions. Each condition's trained +student state is only ever *read* by evaluation. + +All curriculum candidates are validated by the strict TaskValidator +(schema/leakage/solvability/contamination) before use, and the +experiment re-asserts train/eval disjointness defensively. The +experiment performs **zero network calls** — offline by construction. +""" + +from __future__ import annotations + +import copy +import json +import os +import random +import sys +import time + +from ..agents.metargon_agent import MetargonAcademyAgent +from ..envs.gridworld import MechanismGrid +from ..envs.tasks import ( + EpisodeSpec, difficulty_of, make_episodes, +) +from ..eval.metrics import paired_difference +from ..eval.evaluator import Evaluator +from ..learning.loop import run_one_episode +from .diversity import diversity_metrics +from .offline_generator import OfflineTaskGenerator +from .retention import RetentionSuite, sample_substrate +from .selection import ( + LearningProgressSelector, family_key, family_key_json, +) +from .task_registry import TaskRegistry +from .validator import TaskValidator, config_to_proposal + +CONDITIONS = ("uniform", "ascending", "calibrated", "proc_lp", "teacher_lp") +BINS = ("easy", "medium", "hard") + + +# --------------------------------------------------------------------------- +# Candidate preparation (all offline; all strictly validated) +# --------------------------------------------------------------------------- +def procedural_candidates(splits, n: int, seed: int, + validator: TaskValidator) -> list: + """Generate + strictly-validate ``n`` approved procedural candidates + (deterministic, offline; the availability-insurance generator).""" + gen = OfflineTaskGenerator(seed=seed, splits=splits, validator=validator) + results = gen.generate(n, max_attempts=600, source_note="curriculum_v02") + return [r.config for r in results if r.approved] + + +def partition_approved(approved: list, seed: int) -> tuple: + """Deterministically partition approved teacher tasks into + (substrate, holdout, extras), shrinking gracefully when few exist.""" + rng = random.Random(seed) + items = sorted(approved, key=lambda c: str(c.key)) + rng.shuffle(items) + n = len(items) + if n < 2: + items, n = items, n # degenerate: everything to substrate + return items, [], [] + n_sub = min(4, max(1, n // 2)) + n_hold = min(3, max(1, (n - n_sub) // 2)) if n - n_sub >= 2 else \ + min(2, n - n_sub) + sub = items[:n_sub] + hold = items[n_sub:n_sub + n_hold] + extras = items[n_sub + n_hold:] + return sub, hold, extras + + +# --------------------------------------------------------------------------- +# Episode planning per condition +# --------------------------------------------------------------------------- +def _difficulty_rank(config) -> tuple: + return (BINS.index(difficulty_of(config).bin), str(config.key)) + + +def plan_treatment(condition: str, ep_index: int, treatment_pool: list, + rng: random.Random, schedules: dict): + """Return the config for one treatment slot under the condition.""" + if condition == "uniform": + return rng.choice(treatment_pool) + if condition == "ascending": + seq = schedules["ascending"] + return seq[ep_index % len(seq)] + if condition == "calibrated": + bin_name = schedules["schedule"][ep_index] + pool = schedules["by_bin"].get(bin_name) or treatment_pool + return rng.choice(pool) + if condition in ("proc_lp", "teacher_lp"): + idx, _info = schedules["selector"].select(rng) + return treatment_pool[idx] + raise ValueError(f"unknown condition {condition}") + + +def _prepare_calibrated(stream_pool, episodes, seed): + """The Academy's measured-progress procedural curriculum. + + Runs a *scratch* agent per difficulty bin (12 episodes each); the + scratch agent is discarded — only the measured bin statistics are + used to build the schedule. The 36 measurement episodes are NOT + student-training budget and are reported separately.""" + from ..learning.loop import calibrate_curriculum, build_schedule + calib = calibrate_curriculum(stream_pool, episodes_per_bin=12, seed=seed) + schedule = build_schedule(calib, episodes, episodes_per_block=40, + seed=seed + 1) + by_bin = {} + for c in stream_pool: + by_bin.setdefault(difficulty_of(c).bin, []).append(c) + return {"calibration": calib, "calibration_episodes": 3 * 12}, { + "schedule": schedule, "by_bin": by_bin} + + +def _fresh_agent_factory(): + def make(seed): + return MetargonAcademyAgent(seed=seed) + return make + + +# --------------------------------------------------------------------------- +# Core training loop (substrate slots + treatment slots + retention probes) +# --------------------------------------------------------------------------- +def train_condition(condition: str, agent, cfg_e: dict, pools: dict, + episodes: int, seed: int, max_steps: int, + substrate_share: float, retention: RetentionSuite, + retention_teacher: Optional[RetentionSuite], + block_size: int, verbose: bool = True): + """Train one condition's student; probe retention at block ends. + + Returns a detail dict: trained agent, per-episode records, optional + selector, wall time, slot accounting, calibration provenance.""" + rng = random.Random(seed) + substrate_classic = pools["substrate_classic"] + substrate_teacher = pools["substrate_teacher"] + treatment_pool = pools["treatment_pool"] + + schedules = {} + if condition == "ascending": + schedules["ascending"] = sorted(treatment_pool, key=_difficulty_rank) + if condition in ("proc_lp", "teacher_lp"): + schedules["selector"] = LearningProgressSelector( + treatment_pool, seed=seed, + window=int(cfg_e.get("lp_window", 10)), + min_visits=int(cfg_e.get("lp_min_visits", 3)), + lp_weight=float(cfg_e.get("lp_weight", 1.0)), + weakness_weight=float(cfg_e.get("weakness_weight", 1.2)), + novelty_weight=float(cfg_e.get("novelty_weight", 0.8)), + noise_weight=float(cfg_e.get("explore_eps", 0.15)), + weakness_target=float(cfg_e.get("weakness_target", 0.75))) + calib = None + if condition == "calibrated": + calib, schedules = _prepare_calibrated(pools["stream_pool"], + episodes, seed) + + t0 = time.perf_counter() + sel = schedules.get("selector") + records = [] + n_sub = n_classic = n_teacher = n_treat = 0 + last_probe_at = -1 + for ep_i in range(episodes): + # block boundary -> retention probes (evaluation-only) + if ep_i > 0 and ep_i % block_size == 0: + retention.run_probe(agent.save_state(), _fresh_agent_factory(), + episode_i=ep_i) + last_probe_at = ep_i + if retention_teacher is not None and retention_teacher.substrate: + retention_teacher.run_probe( + agent.save_state(), _fresh_agent_factory(), + episode_i=ep_i) + if rng.random() < substrate_share: + if substrate_teacher and rng.random() < 0.3333: + config = rng.choice(substrate_teacher) + source = "substrate_teacher" + n_teacher += 1 + else: + config = rng.choice(substrate_classic) + source = "substrate_classic" + n_classic += 1 + n_sub += 1 + else: + config = plan_treatment(condition, ep_i, treatment_pool, rng, + schedules) + source = "treatment" + n_treat += 1 + spec = EpisodeSpec( + config=config, max_steps=max_steps, noise_q=0.0, + spec_seed=seed * 1000 + ep_i, pool_tag="train", + difficulty=difficulty_of(config)) + rec = run_one_episode(agent, spec) + ok = bool(rec["success"]) + if sel is not None: + sel.record(spec, ok) + records.append({ + "episode": ep_i, "source": source, "success": ok, + "steps": rec["steps"], "family": family_key_json(family_key(config)), + "config": str(config.key), + }) + if last_probe_at != episodes: + retention.run_probe(agent.save_state(), _fresh_agent_factory(), + episode_i=episodes) + if retention_teacher is not None and retention_teacher.substrate: + retention_teacher.run_probe(agent.save_state(), + _fresh_agent_factory(), + episode_i=episodes) + if verbose: + sr = sum(1 for r in records if r["success"]) / max(1, len(records)) + print(f" trained {condition}: train_success={sr:.3f} " + f"(substrate {n_sub} / treatment {n_treat}) " + f"wall={time.perf_counter() - t0:.1f}s") + return { + "condition": condition, + "agent": agent, + "records": records, + "selector": sel, + "wall_s": time.perf_counter() - t0, + "substrate_episodes": n_sub, + "substrate_classic_episodes": n_classic, + "substrate_teacher_episodes": n_teacher, + "treatment_episodes": n_treat, + "calibration": calib, + } + + +# --------------------------------------------------------------------------- +# Evaluation: independent per-config grid (fixes the v0.1 eval artifact) +# --------------------------------------------------------------------------- +def eval_config_grid(agent_state, configs, labels: str, max_steps: int, + n_configs: int, episodes_per_config: int, + seeds, seed: int = 0, pool_tag: str = "eval"): + """Per-config independent evaluation grid. + + The v0.1 experiment evaluated the classic test pool with + ``make_episodes``, which draws ONE config per seed: with n_seeds=3 + the "test result" was really three singleton configs. The archived + 0.000 came from three unlucky draws (fresh agents reproduce the + failure on those same configs — see ``diagnosis.py``), which + therefore said little about the treatment. + + This grid instead evaluates *independently sampled distinct + configs* (matched across conditions via the deterministic sampler), + records the per-config breakdown, and reports both aggregate and + per-config success. Cells are fresh agent instances loaded with the + condition's trained state; nothing is written back to training.""" + picks = sample_substrate(list(configs), n_configs, seed) + rows = [] + for i, cfgc in enumerate(picks): + for seed_i in seeds: + base = seed * 100000 + i * 100 + seed_i + stream = make_episodes([cfgc], pool_tag, base, + episodes_per_config, max_steps=max_steps) + agent = MetargonAcademyAgent(seed=base) + if agent_state is not None: + agent.load_state(agent_state) + succ, steps_total = 0, 0 + for ep, spec in enumerate(stream): + env = MechanismGrid(spec.config, spec.max_steps, spec.noise_q) + obs = env.reset(spec.spec_seed) + agent.episode_start(spec, env.geom) + success = False + steps = 0 + while steps < env.max_steps: + a = agent.act(obs) + before = obs + res = env.step(a) + agent.learn(before, a, res) + steps += 1 + obs = res.observation + if res.terminated: + success = True + break + agent.finish_episode(success) + succ += 1 if success else 0 + steps_total += steps + rows.append({ + "config": str(cfgc.key), "config_i": i, "seed_i": seed_i, + "n": len(stream), + "success_rate": succ / max(1, len(stream)), + "mean_steps": steps_total / max(1, len(stream)), + }) + per_config = {} + for r in rows: + ent = per_config.setdefault(r["config"], {"succ": 0.0, "n": 0}) + ent["succ"] += r["success_rate"] * r["n"] + ent["n"] += r["n"] + per_config = {k: v["succ"] / v["n"] for k, v in per_config.items()} + rates = list(per_config.values()) + summary = { + "n_configs": len(picks), + "episodes_per_config": episodes_per_config, + "seeds": list(seeds), + "config_mean_success": (sum(rates) / len(rates)) if rates else 0.0, + "per_config": {k: round(v, 4) for k, v in per_config.items()}, + } + return {"pool_tag": pool_tag, "summary": summary, + "cells": rows, "per_config_list": rates} + + +def _eval_frozen(agent_state, tag, configs, max_steps, n_seeds, + episodes_per_seed, seed, splits=None): + """Frozen-pool single-config-per-seed streams (legacy v0.1 protocol; + retained only for A/B checks by the diagnosis — the v0.2 experiment + uses ``eval_config_grid``).""" + evaluator = Evaluator( + splits or {"train": []}, seed=seed, + episodes_per_seed=episodes_per_seed, + max_steps=max_steps, pool_tag=tag) + + def make_loaded(s, _st=agent_state): + a = MetargonAcademyAgent(seed=s) + if _st is not None: + a.load_state(_st) + return a + + return evaluator.run_policy(make_loaded, configs, n_seeds=n_seeds) + + +def validator_config_ok(validator: TaskValidator, config) -> bool: + """Defensive re-validation of a registered config (may reject if the + registry moved on since approval — e.g. duplicates).""" + res = validator.validate(config_to_proposal(config)) + return res.approved + + +def _assert_isolation(candidates, forbidden_pools, label): + """Defensive re-assertion of train/eval disjointness.""" + cand_keys = {c.key for c in candidates} + for name, pool in forbidden_pools.items(): + overlap = cand_keys & {c.key for c in pool} + if overlap: + raise RuntimeError( + f"CURRICULUM LEAK ({label}): {len(overlap)} candidate(s) " + f"overlap the {name} pool — refusing to run experiment") + + +def _served_diversity(records: list) -> dict: + fams = {} + for r in records: + fams[r["family"]] = fams.get(r["family"], 0) + 1 + return {"n": len(records), "family_distribution": fams} + + +# --------------------------------------------------------------------------- +# Experiment: matched-budget comparison +# --------------------------------------------------------------------------- +def run_curriculum_experiment( + cfg: dict, splits: dict, registry: TaskRegistry, + results_dir: str, run_id: str = "curriculum-v02", + conditions: tuple = CONDITIONS, episodes: int = 160, + block_size: int = 40, seed: int = 0, max_steps: int = 80, + substrate_share: float = 0.30, n_procedural: int = 24, + verbose: bool = True): + """Run the v0.2 matched-budget curriculum comparison (zero network).""" + e_root = cfg.get("evren", {}) or {} + e = e_root.get("curriculum", {}) or {} + seed = int(e.get("seed", seed)) + substrate_share = float(e.get("substrate_share", substrate_share)) + eval_seed = int(cfg.get("seeds", {}).get("eval", 20261108)) + + results = { + "run_id": run_id, + "conditions": list(conditions), + "episodes": episodes, + "block_size": block_size, + "seed": seed, + "substrate_share": substrate_share, + "api_calls": 0, + "offline": True, + "hash_randomization": bool(sys.flags.hash_randomization), + "python_version": sys.version.split()[0], + "started_at": time.time(), + } + + # ---- candidate pools ------------------------------------------------ + train = splits["train"] + validator = TaskValidator(splits=splits) + approved = [c for c in registry.approved_configs() + if validator_config_ok(validator, c)] + substrate_classic = sample_substrate( + train, int(e.get("substrate_classic_n", 8)), seed + 11) + if approved: + sub_teacher, hold_teacher, extras_teacher = partition_approved( + approved, seed + 12) + else: + sub_teacher, hold_teacher, extras_teacher = [], [], [] + proc_extras = procedural_candidates( + splits, int(e.get("n_procedural", n_procedural)), seed + 13, + validator) + stream_pool = [c for c in train + if c.key not in {s.key for s in substrate_classic}] + + forbidden = {"val": splits["val"], "test": splits["test"], + "ood": splits["ood"]} + _assert_isolation(substrate_classic + sub_teacher + extras_teacher, + forbidden, "curriculum candidates") + _assert_isolation(proc_extras, forbidden, "procedural candidates") + _assert_isolation(hold_teacher + sub_teacher + extras_teacher, + {"train": splits["train"]}, "teacher candidates") + + pools_shared = { + "stream_pool": stream_pool, + "substrate_classic": substrate_classic, + "substrate_teacher": sub_teacher, + "treatment_pool": list(stream_pool), + } + pools_proc = dict(pools_shared) + pools_proc["treatment_pool"] = list(stream_pool) + list(proc_extras) + pools_teacher = dict(pools_shared) + pools_teacher["treatment_pool"] = list(stream_pool) + list(extras_teacher) + condition_pools = { + "uniform": pools_shared, + "ascending": pools_shared, + "calibrated": pools_shared, + "proc_lp": pools_proc, + "teacher_lp": pools_teacher, + } + + # ---- diversity of candidate spaces ----------------------------------- + results["diversity"] = { + "treatment_base": diversity_metrics(stream_pool, reference=train), + "procedural_extras": diversity_metrics(proc_extras, reference=train), + "teacher_extras": diversity_metrics(extras_teacher, reference=train), + "teacher_holdout": diversity_metrics(hold_teacher, reference=train), + "provenance": { + "registered_approved": len(approved), + "registered_source": registry.manifest().get("source"), + "teacher_source": ("registered approved tasks" + if approved else "none (fallback: procedural)"), + }, + } + + # ---- per-condition train + evaluation -------------------------------- + per_condition = {} + for cond in conditions: + cpools = condition_pools[cond] + retention = RetentionSuite( + substrate=substrate_classic, max_steps=max_steps, + episodes_per_config=int(e.get("retention_eps_per_cfg", 2)), + seeds=(0,), label=f"{cond}-classic-substrate") + retention_teacher = RetentionSuite( + substrate=sub_teacher, max_steps=max_steps, + episodes_per_config=int(e.get("retention_eps_per_cfg", 2)), + seeds=(0,), label=f"{cond}-teacher-substrate") + out = train_condition( + cond, MetargonAcademyAgent(seed=seed), e, cpools, + episodes=episodes, seed=seed, max_steps=max_steps, + substrate_share=substrate_share, retention=retention, + retention_teacher=retention_teacher if sub_teacher else None, + block_size=block_size, verbose=verbose) + agent = out["agent"] + state = agent.save_state() + + # final frozen evals; matched grids across conditions + classic = eval_config_grid( + state, splits["test"], "test", max_steps, + int(e.get("test_grid_n", 12)), + int(e.get("eval_eps_per_cfg", 4)), (0, 1), eval_seed) + # persist the trained student state (audit/reproducibility) + policies_dir = os.path.join(results_dir, "policies_v02") + os.makedirs(policies_dir, exist_ok=True) + with open(os.path.join(policies_dir, f"{cond}_agent.json"), + "w", encoding="utf-8") as f: + json.dump(state, f, indent=1, default=str) + teacher_hold = None + if hold_teacher: + teacher_hold = eval_config_grid( + state, hold_teacher, "teacher_holdout", max_steps, + min(len(hold_teacher), 3), + int(e.get("eval_eps_per_cfg", 4)), (0, 1), eval_seed + 1) + ood_probe = eval_config_grid( + state, splits["ood"], "ood", max_steps, + int(e.get("ood_probe_n", 8)), + int(e.get("ood_eps_per_seed", 3)), (0,), eval_seed + 2) + + train_curve = out["records"] + per_condition[cond] = { + "train": { + "success_rate": (sum(1 for r in train_curve if r["success"]) + / max(1, len(train_curve))), + "substrate_episodes": out["substrate_episodes"], + "substrate_classic_episodes": out["substrate_classic_episodes"], + "substrate_teacher_episodes": out["substrate_teacher_episodes"], + "treatment_episodes": out["treatment_episodes"], + "wall_s": round(out["wall_s"], 2), + "calibration_episodes": (out["calibration"] or {}).get( + "calibration_episodes", 0), + }, + "curve": train_curve, + "classic_test": classic["summary"], + "classic_test_per_config": classic["per_config_list"], + "teacher_holdout": (teacher_hold or {}).get("summary"), + "teacher_holdout_per_config": (teacher_hold or {}).get( + "per_config_list"), + "ood_probe": ood_probe["summary"], + "retention_classic": retention.summary(), + "retention_teacher": (retention_teacher.summary() + if sub_teacher else None), + "served_diversity": _served_diversity(train_curve), + "lp_report": out["selector"].progress_report() + if out["selector"] is not None else None, + } + + # ---- paired comparisons ------------------------------------------------ + # Per-config vectors are matched by construction (same deterministic + # config picks per pool), so paired bootstrap resamples config indices. + comparisons = {} + def pair_key(a, b, key_list, label, s): + if a.get(key_list) and b.get(key_list) and len(a[key_list]) == len(b[key_list]): + comparisons[label] = paired_difference(a[key_list], b[key_list], seed=s) + if "uniform" in per_condition and "proc_lp" in per_condition: + pair_key(per_condition["proc_lp"], per_condition["uniform"], + "classic_test_per_config", "lp_effect_classic_test", eval_seed) + pair_key(per_condition["proc_lp"], per_condition["uniform"], + "teacher_holdout_per_config", "lp_effect_teacher_holdout", + eval_seed + 1) + if "proc_lp" in per_condition and "teacher_lp" in per_condition: + pair_key(per_condition["teacher_lp"], per_condition["proc_lp"], + "classic_test_per_config", + "teacher_source_effect_classic_test", eval_seed) + pair_key(per_condition["teacher_lp"], per_condition["proc_lp"], + "teacher_holdout_per_config", + "teacher_source_effect_teacher_holdout", eval_seed + 1) + + results["conditions_detail"] = per_condition + results["comparisons"] = comparisons + results["finished_at"] = time.time() + + os.makedirs(results_dir, exist_ok=True) + path = os.path.join(results_dir, "curriculum_v02_summary.json") + with open(path, "w", encoding="utf-8") as f: + json.dump(_strip_records(results), f, indent=1, default=str) + if verbose: + print(f" summary written to {path}") + return results + + +def _strip_records(results: dict) -> dict: + """Compact serialization: keep summaries; trim very long curves.""" + out = copy.deepcopy(results) + for cond, d in out.get("conditions_detail", {}).items(): + curve = d.get("curve") or [] + if len(curve) > 400: + d["curve"] = curve[:200] + curve[-200:] + return out diff --git a/academy/evren/diagnosis.py b/academy/evren/diagnosis.py new file mode 100644 index 0000000..a27bfb3 --- /dev/null +++ b/academy/evren/diagnosis.py @@ -0,0 +1,210 @@ +"""EVREN curriculum — diagnosis of the v0.1 null result. + +The v0.1 experiment reported classic-pool success = 0.000 for BOTH the +baseline and the teacher condition, while the same agents reached 0.667 +on held-out teacher tasks — an incoherent pattern for "the added tasks +gave no benefit" and worth isolating before building a next-generation +curriculum on top of it. + +Hypotheses this module can test (all offline, deterministic): + + H1 negative transfer: the *persistent* trained state (hypothesis + memory + saturated per-door probe history) actively hurts on + unseen configs, so "trained" agents test worse than fresh ones. + Measurement: same unseen configs, fresh agent vs trained-agent + state, matched probe episodes; plus mechanism counters + (contradicted hypotheses, verified hypotheses, untried-probe + saturation). + + H2 config-specificity: 0.000 is uniform failure vs. a few impossible + seeds. Measurement: per-config success with n episodes each. + + H3 recovery-within-eval: trained agents that fail early can recover + later within the same unseen config once their own experience + accumulates (memory washes out) — measured as success in the + second half vs first half of the probe window. + + H4 exposure confound: teacher-condition training halves classic + exposure; per-classic-family trained success in the *training + curve* differs between conditions. Measured from saved curves. + +Outputs: ``diagnosis_summary.json`` in the EVREN results dir. Zero +network calls. +""" + +from __future__ import annotations + +import json +import os +import random +import time +from typing import Optional + +from ..agents.metargon_agent import MetargonAcademyAgent +from ..envs.gridworld import MechanismGrid +from ..envs.tasks import EpisodeSpec, difficulty_of +from .retention import sample_substrate + +POLICY_PATHS = { + "baseline": os.path.join("policies", "baseline_agent.json"), + "teacher": os.path.join("policies", "teacher_agent.json"), +} + + +def _load_state(path: str) -> Optional[dict]: + if not os.path.exists(path): + return None + with open(path, "r", encoding="utf-8") as f: + return json.load(f) + + +def _probe_config(episodes: int, config, seed_base: int, max_steps: int, + agent_state: Optional[dict], label: str) -> dict: + """Run ``episodes`` episodes on ONE config with an agent whose + persistent state starts at ``agent_state`` (None => fresh).""" + agent = MetargonAcademyAgent(seed=seed_base) + if agent_state is not None: + agent.load_state(agent_state) + spec = EpisodeSpec(config=config, max_steps=max_steps, noise_q=0.0, + spec_seed=seed_base, pool_tag="train", + difficulty=difficulty_of(config)) + episodes_out = [] + succ = 0 + for ep in range(episodes): + env = MechanismGrid(config, max_steps, 0.0) + obs = env.reset(seed_base * 1000 + ep) + agent.episode_start(spec, env.geom) + success = False + steps = 0 + while steps < env.max_steps: + a = agent.act(obs) + before = obs + res = env.step(a) + agent.learn(before, a, res) + steps += 1 + obs = res.observation + if res.terminated: + success = True + break + agent.finish_episode(success) + succ += 1 if success else 0 + episodes_out.append({"success": success, "steps": steps}) + first = episodes_out[:len(episodes_out) // 2] + second = episodes_out[len(episodes_out) // 2:] + diag = agent.diagnostics() + mem = diag["memory"] + tried_n = sum(len(v) for v in agent.tried.values()) + return { + "label": label, + "config": str(config.key), + "episodes": episodes, + "success_rate": succ / episodes, + "first_half": sum(1 for e in first if e["success"]) / max(1, len(first)), + "second_half": sum(1 for e in second if e["success"]) / max(1, len(second)), + "memory": mem, + "tried_entries": tried_n, + "mean_steps": sum(e["steps"] for e in episodes_out) / episodes, + } + + +def run_diagnosis(results_dir: str = None, splits: dict = None, + n_configs: int = 10, episodes: int = 10, + seed: int = 0, max_steps: int = 80, + train_fallback: Optional[int] = None) -> dict: + """Fresh-vs-trained comparison over sampled unseen test configs. + + If saved policy states exist (from the v0.1 experiment), they are + used. Otherwise ``train_fallback`` episodes of quick baseline + training produce a reference trained state (only when explicitly + requested — keeps the routine cheap by default).""" + if splits is None: + from ..envs.tasks import load_splits, DEFAULT_SPLITS_PATH + splits = load_splits(DEFAULT_SPLITS_PATH) + results_dir = results_dir or os.path.join( + os.path.dirname(os.path.dirname(os.path.dirname( + os.path.abspath(__file__)))), + "results", "academy_v01", "evren") + # unseen configs: eval isolation — sample from the frozen test pool + configs = sample_substrate(splits["test"], n_configs, seed + 5) + + states = {} + for name, relpath in POLICY_PATHS.items(): + st = _load_state(os.path.join(results_dir, relpath)) + if st is not None: + states[name] = st + if not states and train_fallback: + networks = _train_fallback_agent(splits, train_fallback, seed, + max_steps) + states["fallback_trained"] = networks + + summary = { + "run_id": "diagnosis", + "seed": seed, + "n_configs": len(configs), + "episodes_per_config": episodes, + "max_steps": max_steps, + "states_measured": sorted(states), + "fresh_agent": [], + "trained_agents": {}, + "api_calls": 0, + "offline": True, + "hash_randomization": bool(__import__("sys").flags.hash_randomization), + "started_at": time.time(), + } + fresh_rows = [_probe_config(episodes, c, seed + 100 + i, max_steps, + None, "fresh") for i, c in enumerate(configs)] + summary["fresh_agent"] = fresh_rows + for name, st in states.items(): + rows = [_probe_config(episodes, c, seed + 200 + i, max_steps, st, + name) for i, c in enumerate(configs)] + summary["trained_agents"][name] = rows + + # aggregates + def agg(rows): + return { + "success_rate": (sum(r["success_rate"] for r in rows) + / max(1, len(rows))), + "mean_memory_hypotheses": (sum(r["memory"]["size"] for r in rows) + / max(1, len(rows))), + "mean_contradicted": (sum( + r["memory"]["status_counts"].get("contradicted", 0) + for r in rows) / max(1, len(rows))), + "mean_verified": (sum( + r["memory"]["status_counts"].get("verified", 0) + for r in rows) / max(1, len(rows))), + "mean_tried_entries": (sum(r["tried_entries"] for r in rows) + / max(1, len(rows))), + "second_half_gain": (sum(r["second_half"] - r["first_half"] + for r in rows) / max(1, len(rows))), + } + summary["aggregate"] = {"fresh": agg(fresh_rows)} + for name, rows in summary["trained_agents"].items(): + summary["aggregate"][name] = agg(rows) + + # paired delta per config: trained - fresh + for name, rows in summary["trained_agents"].items(): + deltas = [t["success_rate"] - f["success_rate"] + for t, f in zip(rows, fresh_rows)] + summary["aggregate"][name]["delta_vs_fresh"] = ( + sum(deltas) / len(deltas)) if deltas else 0.0 + + summary["finished_at"] = time.time() + os.makedirs(results_dir, exist_ok=True) + with open(os.path.join(results_dir, "diagnosis_summary.json"), + "w", encoding="utf-8") as f: + json.dump(summary, f, indent=1, default=str) + return summary + + +def _train_fallback_agent(splits, episodes, seed, max_steps): + """Quick deterministic baseline training for a reference state.""" + from ..learning.loop import run_one_episode + rng = random.Random(seed) + agent = MetargonAcademyAgent(seed=seed) + for i in range(episodes): + cfg = rng.choice(splits["train"]) + spec = EpisodeSpec(config=cfg, max_steps=max_steps, noise_q=0.0, + spec_seed=seed * 1000 + i, pool_tag="train", + difficulty=difficulty_of(cfg)) + run_one_episode(agent, spec) + return agent.save_state() diff --git a/academy/evren/diversity.py b/academy/evren/diversity.py new file mode 100644 index 0000000..ad99a95 --- /dev/null +++ b/academy/evren/diversity.py @@ -0,0 +1,162 @@ +"""EVREN curriculum — diversity and generalization metrics. + +Task-set diversity is measured over the *canonical atom vocabulary* (not +strings): Jaccard distances over atom sets of the two door rules, plus +structural coverage (topology, clause shape, delayed flags, atom-class +multisets). Novelty is measured against a reference pool (usually the +classic training pool). + +All functions are pure and deterministic; no environment interaction. +""" + +from __future__ import annotations + +import json +import math +from itertools import combinations + +from ..envs.gridworld import EnvConfig +from .selection import atom_class, atom_classes_of, family_key, rule_structure + + +def atoms_of(config: EnvConfig) -> frozenset: + """Canonical atom multiset of both door rules (polarity intact).""" + def atoms(rule): + clauses = rule if isinstance(rule, tuple) else (rule,) + return {tuple(a) for clause in clauses for a in clause} + return frozenset(atoms(config.d1_rule) | atoms(config.d2_rule)) + + +def signature(config: EnvConfig) -> dict: + """Canonical JSON-able signature of a configuration.""" + def ser_rule(r): + if isinstance(r, tuple): + return [sorted(map(list, c)) for c in r] + return [sorted(map(list, r))] + return { + "topology": config.topology, + "d1_rule": ser_rule(config.d1_rule), + "d2_rule": ser_rule(config.d2_rule), + "d1_delay": config.d1_delay, + "d2_delay": config.d2_delay, + } + + +def jaccard(a: frozenset, b: frozenset) -> float: + if not a and not b: + return 0.0 + inter = len(a & b) + union = len(a | b) + return 1.0 - (inter / union if union else 1.0) + + +def pairwise_jaccard_stats(configs: list, cap: int = 60) -> dict: + """Mean/min/max pairwise Jaccard distance over atom sets. + + ``cap`` bounds the worst-case pairwise cost deterministically (first + ``cap`` configs in pool order).""" + pool = list(configs)[:cap] + if len(pool) < 2: + return {"mean": 0.0, "min": 0.0, "max": 0.0, "pairs": max(0, len(pool) - 1)} + sets = [atoms_of(c) for c in pool] + dists = [jaccard(sets[i], sets[j]) for i, j in combinations(range(len(pool)), 2)] + return { + "mean": sum(dists) / len(dists), + "min": min(dists), + "max": max(dists), + "pairs": len(dists), + } + + +def _normalized_entropy(counts: list) -> float: + n = sum(counts) + if n <= 0 or len(counts) <= 1: + return 0.0 + h = sum(-c / n * math.log(c / n) for c in counts if c > 0) + max_h = math.log(len(counts)) + return h / max_h if max_h > 0 else 0.0 + + +def family_distribution(configs: list) -> dict: + """family-key -> count over a config list.""" + out = {} + for c in configs: + fk = family_key(c) + k = _key_str(fk) + out[k] = out.get(k, 0) + 1 + return out + + +def coverage_dims(configs: list) -> dict: + """Structural coverage across interpretable dimensions.""" + topologies = {} + shapes = {} # conjunctive vs disjunctive per door + delays = {} + n_atoms = {} + classes = {} + for c in configs: + topologies[c.topology] = topologies.get(c.topology, 0) + 1 + for door, rule in (("d1", c.d1_rule), ("d2", c.d2_rule)): + shape = "conjunctive" if not isinstance(rule, tuple) else "disjunctive" + k = f"{door}:{shape}" + shapes[k] = shapes.get(k, 0) + 1 + delay = (c.d1_delay, c.d2_delay) + delays[str(delay)] = delays.get(str(delay), 0) + 1 + n_atoms[c.n_atoms()] = n_atoms.get(c.n_atoms(), 0) + 1 + for cls in atom_classes_of(c): + k = str(cls) + classes[k] = classes.get(k, 0) + 1 + return { + "topology": topologies, + "clause_shape": shapes, + "delay_flags": delays, + "n_atoms": {str(k): v for k, v in sorted(n_atoms.items())}, + "atom_classes": classes, + } + + +def _key_str(fk: tuple) -> str: + def lst(x): + if isinstance(x, frozenset): + return {"$set": [lst(v) for v in sorted(x, key=str)]} + if isinstance(x, (tuple, list)): + return [lst(v) for v in x] + return x + return json.dumps(lst(fk)) + + +def diversity_metrics(configs: list, reference=None, cap: int = 60) -> dict: + """Full diversity report for a candidate/curriculum pool. + + ``reference`` (optional): pool for novelty measurement (e.g. the + classic train pool — measuring how far the teacher/curriculum tasks + sit from everything the procedural pipeline already offers).""" + n = len(configs) + fams = family_distribution(configs) + keys = [c.key for c in configs] + unique_sigs = len(set(keys)) if n else 0 + fam_ent = _normalized_entropy(list(fams.values())) if fams else 0.0 + out = { + "n": n, + "unique_configs": unique_sigs, + "unique_ratio": (unique_sigs / n) if n else 0.0, + "families": fams, + "family_entropy_normalized": fam_ent, + "pairwise_jaccard": pairwise_jaccard_stats(configs, cap=cap), + "coverage": coverage_dims(configs), + } + if reference: + ref_sets = [atoms_of(c) for c in list(reference)[:cap]] + nov = [] + for c in configs: + if not ref_sets: + nov.append(1.0) + continue + nov.append(min(jaccard(atoms_of(c), rs) for rs in ref_sets) if ref_sets else 0.0) + out["novelty_vs_reference"] = { + "mean": (sum(nov) / len(nov)) if nov else 0.0, + "min": min(nov) if nov else 0.0, + "max": max(nov) if nov else 0.0, + "reference_n": len(reference), + } + return out diff --git a/academy/evren/retention.py b/academy/evren/retention.py new file mode 100644 index 0000000..626ea5b --- /dev/null +++ b/academy/evren/retention.py @@ -0,0 +1,143 @@ +"""EVREN curriculum — retention (anti-forgetting) protocol. + +The v0.1 experiment measured "forgetting" with a *single* post-hoc probe +whose result was identical for every condition (delta = +0.000). That is +uninformative: a one-shot probe cannot separate true retention of +previously-learned content from incidental later exposure. + +This module implements the standard continual-learning measurement: + + * a **frozen substrate** — the same small task set for every condition, + which every condition trains on with *matched exposure*; + * **periodic probes** — the substrate is re-measured at fixed block + boundaries with fresh evaluation episodes (never inside the training + stream), giving a retention *curve* rather than one number; + * **retention deltas** — late mean minus early mean per substrate item + and in aggregate (positive = improved/stable, negative = forgotten). + +The retention suite operates on classic *training-pool* configs. It never +touches val/test/OOD pools, and the probes never feed back into training. +""" + +from __future__ import annotations + +import random + +from ..envs.gridworld import MechanismGrid +from ..envs.tasks import EpisodeSpec, difficulty_of +from ..eval.metrics import summarize_episodes + + +def sample_substrate(pool: list, n: int, seed: int) -> list: + """Deterministically sample a frozen substrate subset from a training + pool (drawn *without* replacement; capped at the pool size).""" + rng = random.Random(seed) + items = sorted(pool, key=lambda c: str(c.key)) + rng.shuffle(items) + return items[:max(0, min(n, len(items)))] + + +class RetentionSuite: + """Frozen retention substrate + matched probe runner.""" + + def __init__(self, substrate: list, max_steps: int = 80, + episodes_per_config: int = 4, seeds=(0, 1), + label: str = "substrate"): + self.substrate = list(substrate) + self.max_steps = max_steps + self.episodes_per_config = episodes_per_config + self.seeds = tuple(seeds) + self.label = label + self.snaps = [] # [{block, records, summary}] + + # -- probing ------------------------------------------------------------ + def run_probe(self, agent_state: dict, agent_factory, episode_i: int) -> dict: + """Measure current performance on the frozen substrate. + + ``agent_factory(seed)`` builds a fresh agent; ``agent_state`` is + the *current* persistent training state (loaded into each probe + instance). Probe episodes are evaluation-only: nothing is + recorded back into training, and matched streams make probes + comparable across conditions.""" + records = [] + ep = 0 + for cfg in self.substrate: + for seed_i in self.seeds: + seed = seed_i * 7 + 911 + for _ in range(self.episodes_per_config): + spec = EpisodeSpec( + config=cfg, max_steps=self.max_steps, noise_q=0.0, + spec_seed=seed * 1000 + ep, pool_tag="retention_probe", + difficulty=difficulty_of(cfg)) + ep += 1 + agent = agent_factory(seed + ep) + if agent_state is not None: + agent.load_state(agent_state) + env = MechanismGrid(spec.config, spec.max_steps, + spec.noise_q) + obs = env.reset(spec.spec_seed) + agent.episode_start(spec, env.geom) + success = False + steps = 0 + while steps < env.max_steps: + a = agent.act(obs) + before = obs + res = env.step(a) + agent.learn(before, a, res) + steps += 1 + obs = res.observation + if res.terminated: + success = True + break + agent.finish_episode(success) + records.append({ + "success": success, "steps": steps, + "episode": episode_i, + "seed": seed, + "config": str(spec.config.key), + }) + summary = summarize_episodes(records) + self.snaps.append({"episode_seen": episode_i, "episode_count": None, + "summary": {k: v for k, v in summary.items()}, + "records": records}) + return self.snaps[-1] + + # -- analysis ----------------------------------------------------------- + def retention_delta(self) -> float: + """success_rate(last snapshot) - success_rate(first snapshot).""" + if len(self.snaps) < 2: + return 0.0 + return (self.snaps[-1]["summary"]["success_rate"] + - self.snaps[0]["summary"]["success_rate"]) + + def per_config_delta(self) -> dict: + """Late minus early success per substrate configuration.""" + if not self.snaps: + return {} + def bykey(snap): + out = {} + for r in snap["records"]: + out.setdefault(r["config"], []).append(1.0 if r["success"] else 0.0) + return {k: sum(v) / len(v) for k, v in out.items()} + first, last = bykey(self.snaps[0]), bykey(self.snaps[-1]) + out = {} + for k in sorted(set(first) | set(last)): + a = first.get(k, 0.0) + b = last.get(k, 0.0) + out[k] = {"first": a, "last": b, "delta": b - a} + return out + + def summary(self) -> dict: + return { + "label": self.label, + "substrate_n": len(self.substrate), + "substrate_keys": [str(c.key) for c in self.substrate], + "snapshots": [ + {"episode_seen": s["episode_seen"], + "success_rate": s["summary"]["success_rate"], + "mean_steps": s["summary"]["mean_steps"], + "n": s["summary"]["n"]} + for s in self.snaps], + "retention_delta": self.retention_delta(), + "per_config": self.per_config_delta(), + } diff --git a/academy/evren/selection.py b/academy/evren/selection.py new file mode 100644 index 0000000..6517e68 --- /dev/null +++ b/academy/evren/selection.py @@ -0,0 +1,403 @@ +"""EVREN curriculum — adaptive task selection. + +The v0.1 experiment dropped teacher-approved tasks into the training +stream at a fixed probability (``p_teacher``). That is *task-set +augmentation*, not a curriculum: the stream was order-blind and ignored +what the student actually did. This module makes selection adaptive: + + * **learning progress** — tasks whose *family* is measured to be + improving fastest (|Δ success| between the two halves of a bounded + sliding window) are preferred, which keeps the student working at + the frontier of its competence; + * **weakness targeting** — families and atom classes where measured + success is furthest below target receive a stable boost, so + chronically failing structures are not starved by their own low + success rate; + * **novelty recovery** — families visited rarely get a bonus until + they have enough evidence to be scored. + +Everything is deterministic under a seed: scores derive only from +recorded outcomes, and all randomness flows through one seeded +``random.Random``. The selector never sees evaluation pools; callers +pass exactly the candidates that are legal to train on (isolation is +enforced upstream by the validator and re-asserted by the experiment). +""" + +from __future__ import annotations + +import json +import random +from collections import deque +from typing import Optional + +from ..ckpt.checkpoint import rng_state_safe, rng_from_state +from ..envs.gridworld import EnvConfig +from ..envs.tasks import EpisodeSpec + +DEFAULTS = { "window": 10, # sliding-window length per family + "min_visits": 3, # visits before a family is "scored" + "lp_weight": 1.0, + "weakness_weight": 1.2, + "novelty_weight": 0.8, + "noise_weight": 0.15, # exploration floor (uniform slot probability) + "soft_temperature": 0.15, # softmax temperature over scores + "weakness_target": 0.75, # success target for weakness deficit +} + +# --------------------------------------------------------------------------- +# Structural signatures (families for learning progress) +# --------------------------------------------------------------------------- +def atom_class(atom) -> tuple: + """Abstract an atom to its structural class. + + Switches collapse to a polarity class (which switch is irrelevant); + plates keep their *kind* (load / loadany / empty); ``load`` keeps the + object identity because "load P1 with A" and "with B" are different + skills for the student. + """ + kind = atom[0] + if kind == "sw": + return ("sw", bool(atom[2])) + if kind == "load": + return ("load", atom[2]) + return (kind,) # loadany / empty + + +def _classes(clause) -> tuple: + return tuple(sorted(atom_class(a) for a in clause)) + + +def rule_structure(rule) -> tuple: + """Structural form of a rule: clauses become atom-class tuples, in + canonical order (conjunctive single clause stays a single clause).""" + if isinstance(rule, tuple): + return tuple(sorted(_classes(clause) for clause in rule)) + return _classes(rule) + + +def family_key(config: EnvConfig) -> tuple: + """Canonical family signature of a task configuration. + + Two configs share a family when their two door rules have the same + abstract structure pair (atom classes, arity, conjunctive/ + disjunctive shape) and the same topology. The door roles (D1/D2) + are unordered — swapping which door carries which rule does not make + it a different *skill* — and the delay flags are excluded (they + modulate observability, not the causal structure to be learned). + Family-level stats pool evidence that any single singleton config + would split too thin.""" + return ( + config.topology, + frozenset({rule_structure(config.d1_rule), + rule_structure(config.d2_rule)}), + ) + + +def atom_classes_of(config: EnvConfig) -> set: + """All atom classes occurring in either rule (weakness tracking).""" + def classes(rule): + clauses = rule if isinstance(rule, tuple) else (rule,) + return {atom_class(a) for clause in clauses for a in clause} + return classes(config.d1_rule) | classes(config.d2_rule) + + +# --------------------------------------------------------------------------- +# Family statistics +# --------------------------------------------------------------------------- +class FamilyStats: + """Bounded sliding-window success record for one family.""" + + def __init__(self, window: int, min_visits: int): + self.window = window + self.min_visits = min_visits + self.results = deque(maxlen=window) + self.visits = 0 + self.last_probe_step = 0 + + def record(self, outcome: bool, step: int) -> None: + self.results.append(1.0 if outcome else 0.0) + self.visits += 1 + self.last_probe_step = step + + # -- measurements ------------------------------------------------------ + def success_rate(self) -> Optional[float]: + """Success rate over the window (None before ``min_visits``).""" + if self.visits < self.min_visits: + return None + return sum(self.results) / len(self.results) + + def learning_progress(self) -> Optional[float]: + """|Δ success| between the two halves of the window. + + Absolute learning progress: a family whose success rate is + *moving* — up or down — is an active frontier. Families with + stable success (already mastered, or hopeless) score low and are + de-prioritized in favour of frontiers.""" + if len(self.results) < 4: + return None + half = len(self.results) // 2 + first = list(self.results)[:half] + second = list(self.results)[half:] + if not first or not second: + return None + delta = (sum(second) / len(second)) - (sum(first) / len(first)) + return abs(delta) + + def weakness(self, target: float) -> Optional[float]: + """Deficit below the measured-success target (>= 0).""" + sr = self.success_rate() + if sr is None: + return None + return max(0.0, target - sr) + + def as_dict(self) -> dict: + return { + "visits": self.visits, + "results": list(self.results), + "last_probe_step": self.last_probe_step, + } + + @classmethod + def from_dict(cls, d: dict, window: int, min_visits: int) -> "FamilyStats": + st = cls(window=window, min_visits=min_visits) + st.visits = int(d["visits"]) + for x in d["results"]: + st.results.append(x) + st.last_probe_step = int(d.get("last_probe_step", 0)) + return st + + +class WeaknessTracker: + """Per-atom-class success statistics across all recorded episodes. + + A class is *weak* when episodes whose tasks contain it succeed well + below target — e.g. absence atoms (``empty``) or object-specific + loads. The selector boosts candidates containing weak classes, so + the curriculum adapts to *student weaknesses* rather than to a fixed + syllabus.""" + + def __init__(self, window: int, min_visits: int, target: float): + self.window = window + self.min_visits = min_visits + self.target = target + self.stats: dict = {} + + def record(self, config: EnvConfig, outcome: bool, step: int) -> None: + for cls in atom_classes_of(config): + s = self.stats.setdefault( + cls, FamilyStats(self.window, self.min_visits)) + s.record(outcome, step) + + def weakness_of(self, cls) -> float: + s = self.stats.get(cls) + if s is None: + return 0.0 # unknown class: no boost (yet) + w = s.weakness(self.target) + return w if w is not None else 0.0 + + def summary(self) -> list: + rows = [] + for cls, s in sorted(self.stats.items(), key=lambda kv: str(kv[0])): + w = s.weakness(self.target) + rows.append({ + "atom_class": list(cls), + "visits": s.visits, + "success_rate": s.success_rate(), + "weakness": w if w is not None else 0.0, + }) + rows.sort(key=lambda r: -r["weakness"]) + return rows + + def as_dict(self) -> dict: + return {"target": self.target, "stats": { + json.dumps(list(cls)): st.as_dict() + for cls, st in self.stats.items()}} + + @classmethod + def from_dict(cls, d: dict, window: int, min_visits: int) -> "WeaknessTracker": + tr = cls(window=window, min_visits=min_visits, + target=d["target"]) + for k, v in d["stats"].items(): + tr.stats[_json_key_to_class(k)] = FamilyStats.from_dict( + v, window, min_visits) + return tr + + +# --------------------------------------------------------------------------- +# Selector +# --------------------------------------------------------------------------- +def family_key_json(fk: tuple) -> str: + """Canonical JSON string form of a family key (for records).""" + return json.dumps(_lists(fk)) + + +def _json_key_to_family(s: str) -> tuple: + """Rebuild a canonical family key from its JSON serialization.""" + return _tuples(json.loads(s)) + + +def _family_to_json(fk: tuple) -> str: + return json.dumps(_lists(fk)) + + +def _lists(x): + if isinstance(x, frozenset): + # canonical, deterministic order for JSON + return {"$set": [_lists(v) for v in sorted(x, key=str)]} + if isinstance(x, (tuple, list)): + return [_lists(v) for v in x] + return x + + +def _tuples(x): + if isinstance(x, dict) and set(x) == {"$set"}: + return frozenset(_tuples(v) for v in x["$set"]) + if isinstance(x, (tuple, list)): + return tuple(_tuples(v) for v in x) + return x + + +def _json_key_to_class(s: str) -> tuple: + return _tuples(json.loads(s)) + + +class LearningProgressSelector: + """Adaptive curriculum selector over a fixed candidate pool. + + Score of a candidate = lp_weight * |ΔPR(family)| + weakness_weight * + Σ weakness(atom classes of the family) (+ novelty bonus for young + families), resolved by softmax over candidates with a uniform + exploration floor. + """ + + def __init__(self, candidates: list, seed: int = 0, + **overrides): + unknown = set(overrides) - set(DEFAULTS) + if unknown: + raise ValueError(f"unknown selector params: {sorted(unknown)}") + self.params = dict(DEFAULTS) + self.params.update(overrides) + self.seed = seed + self.rng = random.Random(seed) + self.candidates = list(candidates) + self._family_of = [family_key(c) for c in self.candidates] + self.family_stats: dict = {} + self.weakness = WeaknessTracker( + window=self.params["window"], + min_visits=self.params["min_visits"], + target=self.params["weakness_target"]) + self._episodes = 0 + self.choice_history = [] + self.last_reason = None # introspection for audit + + # -- recording -------------------------------------------------------- + def record(self, spec: EpisodeSpec, outcome: bool) -> None: + """Record the outcome of an episode (any source).""" + self._episodes += 1 + step = self._episodes + fk = family_key(spec.config) + st = self.family_stats.setdefault( + fk, FamilyStats(self.params["window"], self.params["min_visits"])) + st.record(outcome, step) + self.weakness.record(spec.config, outcome, step) + + # -- scoring ----------------------------------------------------------- + def family_score(self, fk) -> float: + """Composite score for a family (higher = select more).""" + st = self.family_stats.get(fk) + if st is None or st.visits < self.params["min_visits"]: + return self.params["novelty_weight"] # young family bonus + score = 0.0 + lp = st.learning_progress() + if lp is not None: + score += self.params["lp_weight"] * lp + wk = st.weakness(self.params["weakness_target"]) + if wk is not None: + score += self.params["weakness_weight"] * wk + return score + + def candidate_score(self, idx: int) -> float: + """Score of candidate index, including a tiny deterministic + jitter to break within-family ties.""" + base = self.family_score(self._family_of[idx]) + return base + self.params["soft_temperature"] * self.rng.random() * 1e-3 + + score = candidate_score # backwards-friendly alias + + # -- selection --------------------------------------------------------- + def select(self, rng: Optional[random.Random] = None) -> tuple: + """Pick the next candidate index: softmax over scores with an + exploration floor. Returns (index, info).""" + rng = rng or self.rng + if not self.candidates: + raise ValueError("selector has no candidates") + if rng.random() < self.params["noise_weight"]: + idx = rng.randrange(len(self.candidates)) + self.last_reason = "explore" + else: + scores = [self.candidate_score(i) for i in range(len(self.candidates))] + m = max(scores) + t = max(self.params["soft_temperature"], 1e-9) + weights = [pow(2.718281828459045, (s - m) / t) for s in scores] + total = sum(weights) + r = rng.random() * total + acc = 0.0 + idx = len(scores) - 1 + for i, w in enumerate(weights): + acc += w + if r <= acc: + idx = i + break + self.last_reason = "scored" + self.choice_history.append(idx) + fk = self._family_of[idx] + info = {"family": fk, "score": self.family_score(fk), + "reason": self.last_reason} + return idx, info + + # -- introspection ----------------------------------------------------- + def progress_report(self) -> dict: + rows = [] + for fk, st in sorted(self.family_stats.items(), + key=lambda kv: _family_to_json(kv[0])): + rows.append({ + "family_json": _family_to_json(fk), + "visits": st.visits, + "success_rate": st.success_rate(), + "learning_progress": st.learning_progress(), + }) + rows.sort(key=lambda r: -r["visits"]) + return { + "episodes": self._episodes, + "families": rows, + "weakness": self.weakness.summary(), + } + + # -- checkpointing ----------------------------------------------------- + def save_state(self) -> dict: + return { + "params": self.params, + "seed": self.seed, + "rng": rng_state_safe(self.rng), + "family_stats": { + _family_to_json(fk): st.as_dict() + for fk, st in self.family_stats.items()}, + "weakness": self.weakness.as_dict(), + "episodes": self._episodes, + "choice_history": list(self.choice_history), + } + + def load_state(self, state: dict) -> None: + self.params = dict(state["params"]) + self.seed = state["seed"] + rng_from_state(self.rng, state) + self.family_stats = { + _json_key_to_family(k): FamilyStats.from_dict( + v, self.params["window"], self.params["min_visits"]) + for k, v in state["family_stats"].items()} + self.weakness = WeaknessTracker.from_dict( + state["weakness"], self.params["window"], + self.params["min_visits"]) + self._episodes = int(state["episodes"]) + self.choice_history = list(state["choice_history"]) + self.last_reason = None diff --git a/academy/tests/test_evren_curriculum_experiment.py b/academy/tests/test_evren_curriculum_experiment.py new file mode 100644 index 0000000..8e6eb5f --- /dev/null +++ b/academy/tests/test_evren_curriculum_experiment.py @@ -0,0 +1,167 @@ +"""Tests for the v0.2 curriculum experiment (tiny smoke budgets).""" + +from __future__ import annotations + +import json +import os +import shutil +import tempfile +import unittest + +from academy.envs.gridworld import EnvConfig +from academy.envs.tasks import get_splits +from academy.evren.curriculum_experiment import ( + eval_config_grid, partition_approved, plan_treatment, + run_curriculum_experiment, +) +from academy.evren.task_registry import TaskRegistry + +C1 = EnvConfig("TP1", frozenset({("sw", "S2", True)}), + frozenset({("load", "P2", "B")}), False, False) +C2 = EnvConfig("TP2", frozenset({("empty", "P1")}), + frozenset({("sw", "S1", False)}), True, True) + + +def _tiny_cfg(): + return {"evren": {"curriculum": { + "seed": 5, + "substrate_classic_n": 4, + "n_procedural": 4, + "substrate_share": 0.30, + "retention_eps_per_cfg": 1, + "test_grid_n": 4, + "eval_eps_per_cfg": 2, + "holdout_eps_per_seed": 2, + "ood_probe_n": 2, + "ood_eps_per_seed": 1, + }}, "seeds": {"eval": 20261108}} + + +class TestEvalGrid(unittest.TestCase): + def test_matched_picks_across_calls(self): + splits = get_splits() + a = eval_config_grid(None, splits["test"], "test", 40, 5, 1, (0,), 7) + b = eval_config_grid(None, splits["test"], "test", 40, 5, 1, (0,), 7) + self.assertEqual([c["config"] for c in a["cells"]], + [c["config"] for c in b["cells"]]) + self.assertEqual(len(a["cells"]), 5) # n_configs x 1 seed + + def test_summary_per_config(self): + splits = get_splits() + res = eval_config_grid(None, splits["test"], "test", 40, 4, 2, (0, 1), + 3) + self.assertEqual(res["summary"]["n_configs"], 4) + self.assertEqual(len(res["per_config_list"]), 4) + for r in res["cells"]: + self.assertIn(r["config"], res["summary"]["per_config"]) + + +class TestPartition(unittest.TestCase): + def test_partition_shapes(self): + sub, hold, extras = partition_approved([C1, C2, C1, C2], seed=1) + self.assertEqual(len(sub) + len(hold) + len(extras), 4) + self.assertGreaterEqual(len(sub), 1) + allc = sub + hold + extras + self.assertEqual(sorted(str(c.key) for c in allc), + sorted(str(c.key) for c in [C1, C2, C1, C2])) + + def test_degenerate_single(self): + sub, hold, extras = partition_approved([C1], seed=0) + self.assertEqual((len(sub), len(hold), len(extras)), (1, 0, 0)) + + +class TestPlanTreatment(unittest.TestCase): + def test_accepts_known_conditions(self): + import random + rng = random.Random(0) + pool = [C1, C2] + self.assertIn(plan_treatment("uniform", 0, pool, rng, {}), + pool) + sched = {"ascending": sorted(pool, key=str)} + self.assertIn(plan_treatment("ascending", 0, pool, rng, sched), pool) + self.assertIn(plan_treatment("ascending", 1, pool, rng, sched), pool) + + def test_unknown_condition_raises(self): + import random + with self.assertRaises(ValueError): + plan_treatment("bogus", 0, [C1], random.Random(0), {}) + + +class TestExperimentSmoke(unittest.TestCase): + def setUp(self): + self.tmp = tempfile.mkdtemp(prefix="curriculum-smoke-") + self.addCleanup(shutil.rmtree, self.tmp, ignore_errors=True) + self.results_dir = os.path.join(self.tmp, "evren") + self.registry_dir = os.path.join(self.tmp, "registry") + + def test_two_conditions_matched_budgets_and_isolation(self): + splits = get_splits() + registry = TaskRegistry(self.registry_dir, run_id="test") + results = run_curriculum_experiment( + _tiny_cfg(), splits, registry, results_dir=self.results_dir, + conditions=("uniform", "proc_lp"), episodes=6, block_size=3, + seed=5, n_procedural=4, verbose=False) + # summary persisted + parseable + path = os.path.join(self.results_dir, "curriculum_v02_summary.json") + self.assertTrue(os.path.exists(path)) + with open(path, "r", encoding="utf-8") as f: + reloaded = json.load(f) + detail = reloaded["conditions_detail"] + self.assertEqual(set(detail) | set(), set(detail)) + # matched budgets: every condition saw exactly `episodes` episodes + for cond, d in detail.items(): + n = d["train"]["substrate_episodes"] + d["train"]["treatment_episodes"] + self.assertEqual(n, 6) + # offline / zero-API assertions + self.assertEqual(reloaded["api_calls"], 0) + self.assertTrue(reloaded["offline"]) + # isolation: no served config may be in val/test/ood pools + def keys(pool): + return {str(c.key) for c in splits[pool]} + forbidden = keys("val") | keys("test") | keys("ood") + for cond, d in detail.items(): + for r in d["curve"]: + self.assertNotIn(r["config"], forbidden) + # eval grids are matched across conditions (identical picks) + cu = detail["uniform"]["classic_test_per_config"] + cp = detail["proc_lp"]["classic_test_per_config"] + self.assertEqual(len(cu), len(cp)) + # trained student states persisted for audit (one per condition) + policies_dir = os.path.join(self.results_dir, "policies_v02") + self.assertTrue(os.path.isdir(policies_dir)) + for cond in ("uniform", "proc_lp"): + p = os.path.join(policies_dir, f"{cond}_agent.json") + self.assertTrue(os.path.exists(p)) + with open(p, "r", encoding="utf-8") as f: + st = json.load(f) + self.assertIn("memory", st) + self.assertIn("rng", st) + # comparisons computed for the proc_lp - uniform pair + self.assertIn("lp_effect_classic_test", reloaded["comparisons"]) + cmp_ = reloaded["comparisons"]["lp_effect_classic_test"] + self.assertIn("mean", cmp_) + self.assertIn("ci", cmp_) + + def test_empty_registry_teacher_lp_falls_back(self): + splits = get_splits() + registry = TaskRegistry(self.registry_dir, run_id="test2") + results = run_curriculum_experiment( + _tiny_cfg(), splits, registry, results_dir=self.results_dir, + conditions=("teacher_lp",), episodes=4, block_size=4, + seed=6, n_procedural=3, verbose=False) + d = results["conditions_detail"]["teacher_lp"] + self.assertEqual( + d["train"]["substrate_episodes"] + d["train"]["treatment_episodes"], + 4) + self.assertIsNone(results["conditions_detail"]["teacher_lp"] + ["teacher_holdout"]) + self.assertEqual( + results["diversity"]["provenance"]["registered_approved"], 0) + # teacher_lp with no registered tasks still persists its state + p = os.path.join(self.results_dir, "policies_v02", + "teacher_lp_agent.json") + self.assertTrue(os.path.exists(p)) + + +if __name__ == "__main__": + unittest.main() diff --git a/academy/tests/test_evren_diagnosis.py b/academy/tests/test_evren_diagnosis.py new file mode 100644 index 0000000..aa374ca --- /dev/null +++ b/academy/tests/test_evren_diagnosis.py @@ -0,0 +1,67 @@ +"""Tests for the v0.1 null-result diagnosis (offline, tiny budgets).""" + +from __future__ import annotations + +import json +import os +import shutil +import tempfile +import unittest + +from academy.envs.tasks import get_splits +from academy.evren.diagnosis import run_diagnosis + + +class TestDiagnosis(unittest.TestCase): + def setUp(self): + self.tmp = tempfile.mkdtemp(prefix="diagnosis-smoke-") + self.addCleanup(shutil.rmtree, self.tmp, ignore_errors=True) + + def test_runs_with_fallback_training(self): + splits = get_splits() + s = run_diagnosis(results_dir=self.tmp, splits=splits, + n_configs=3, episodes=3, seed=11, + train_fallback=2, max_steps=60) + self.os_checks(s) + + def os_checks(self, s): + self.assertTrue(s["offline"]) + self.assertEqual(s["api_calls"], 0) + self.assertEqual(s["n_configs"], 3) + agg = s["aggregate"] + self.assertIn("fresh", agg) + fresh = agg["fresh"] + for key in ("success_rate", "delta_vs_fresh"): + # fresh has no trained-state to differ from + self.assertNotIn(key, fresh) if key == "delta_vs_fresh" else \ + self.assertIn(key, fresh) + # fallback trained state measured + self.assertIn("fallback_trained", s["trained_agents"]) + tr = agg["fallback_trained"] + for key in ("success_rate", "mean_contradicted", "mean_verified", + "mean_tried_entries", "delta_vs_fresh"): + self.assertIn(key, tr) + self.assertAlmostEqual(tr["delta_vs_fresh"], + tr["success_rate"] - fresh["success_rate"], + places=9) + # summary persisted + path = os.path.join(self.tmp, "diagnosis_summary.json") + self.assertTrue(os.path.exists(path)) + with open(path, "r", encoding="utf-8") as f: + reloaded = json.load(f) + self.assertEqual(reloaded["run_id"], "diagnosis") + + def test_no_states_is_reported_cleanly(self): + # without fallback training and without saved policies the + # diagnosis still measures the fresh agent only + splits = get_splits() + s = run_diagnosis(results_dir=self.tmp, splits=splits, + n_configs=2, episodes=2, seed=13, + train_fallback=None, max_steps=60) + self.assertEqual(s["states_measured"], []) + self.assertEqual(len(s["fresh_agent"]), 2) + self.assertTrue(s["offline"]) + + +if __name__ == "__main__": + unittest.main() diff --git a/academy/tests/test_evren_diversity.py b/academy/tests/test_evren_diversity.py new file mode 100644 index 0000000..2b23e5b --- /dev/null +++ b/academy/tests/test_evren_diversity.py @@ -0,0 +1,85 @@ +"""Tests for diversity / generalization metrics.""" + +from __future__ import annotations + +import unittest + +from academy.envs.gridworld import EnvConfig +from academy.evren.diversity import ( + _normalized_entropy, atoms_of, coverage_dims, diversity_metrics, + family_distribution, jaccard, signature, +) + + +C1 = EnvConfig("TP1", frozenset({("sw", "S2", True)}), + frozenset({("load", "P2", "B")}), False, False) +C2 = EnvConfig("TP1", frozenset({("empty", "P1")}), + frozenset({("sw", "S1", False)}), True, True) +C3 = EnvConfig("TP2", frozenset({("sw", "S2", True)}), + frozenset({("load", "P2", "B")}), False, False) + + +class TestAtoms(unittest.TestCase): + def test_atoms_of_union(self): + a = atoms_of(C1) + self.assertEqual(a, frozenset({("sw", "S2", True), ("load", "P2", "B")})) + + def test_jaccard_bounds(self): + self.assertEqual(jaccard(frozenset(), frozenset()), 0.0) + self.assertEqual(jaccard(frozenset({1}), frozenset({1})), 0.0) + self.assertEqual(jaccard(frozenset({1}), frozenset({2})), 1.0) + + +class TestSignature(unittest.TestCase): + def test_canonical_and_jsonable(self): + import json + s = signature(C1) + json.dumps(s) # must be serializable + self.assertEqual(s["topology"], "TP1") + self.assertFalse(s["d1_delay"]) + + +class TestMetrics(unittest.TestCase): + def test_normalized_entropy_bounds(self): + self.assertEqual(_normalized_entropy([]), 0.0) + self.assertEqual(_normalized_entropy([5]), 0.0) + self.assertAlmostEqual(_normalized_entropy([1, 1]), 1.0) + + def test_family_distribution_counts(self): + fams = family_distribution([C1, C1, C3]) + # C1 and C3 differ only by topology => distinct families + self.assertEqual(sum(fams.values()), 3) + self.assertEqual(len(fams), 2) + + def test_unique_ratio_and_jaccard_stats(self): + dm = diversity_metrics([C1, C2, C3]) + self.assertEqual(dm["n"], 3) + self.assertEqual(dm["unique_configs"], 3) + self.assertEqual(dm["unique_ratio"], 1.0) + self.assertGreaterEqual(dm["pairwise_jaccard"]["mean"], 0.0) + self.assertLessEqual(dm["pairwise_jaccard"]["max"], 1.0) + + def test_novelty_vs_reference(self): + ref = [C2] + same = diversity_metrics([C2], reference=ref) + self.assertAlmostEqual(same["novelty_vs_reference"]["mean"], 0.0) + disjoint = diversity_metrics( + [EnvConfig("TP1", frozenset({("load", "P2", "B")}), + frozenset({("loadany", "P2")}), False, False)], + reference=ref) + self.assertEqual(disjoint["novelty_vs_reference"]["min"], 1.0) + + def test_coverage_dims_content(self): + cov = coverage_dims([C1, C2]) + self.assertEqual(cov["topology"], {"TP1": 2}) + self.assertEqual(cov["delay_flags"], {"(False, False)": 1, + "(True, True)": 1}) + self.assertIn("d1:conjunctive", cov["clause_shape"]) + + def test_duplicate_ratio(self): + dm = diversity_metrics([C1, C1, C1]) + self.assertEqual(dm["unique_ratio"], 1 / 3) + + +if __name__ == "__main__": + unittest.main() diff --git a/academy/tests/test_evren_retention.py b/academy/tests/test_evren_retention.py new file mode 100644 index 0000000..84e667d --- /dev/null +++ b/academy/tests/test_evren_retention.py @@ -0,0 +1,100 @@ +"""Tests for the retention (anti-forgetting) protocol.""" + +from __future__ import annotations + +import unittest + +from academy.agents.metargon_agent import MetargonAcademyAgent +from academy.envs.gridworld import EnvConfig +from academy.envs.tasks import get_splits +from academy.evren.retention import RetentionSuite, sample_substrate + + +def _suite_from(pools): + return pools + + +class TestSampleSubstrate(unittest.TestCase): + def test_deterministic_subset(self): + splits = get_splits() + a = sample_substrate(splits["train"], 5, seed=1) + b = sample_substrate(splits["train"], 5, seed=1) + self.assertEqual([str(c.key) for c in a], [str(c.key) for c in b]) + self.assertEqual(len(a), 5) + keys = {str(c.key) for c in a} + self.assertEqual(len(keys), 5) # no duplicates + + def test_subset_of_pool(self): + splits = get_splits() + pool_keys = {str(c.key) for c in splits["train"]} + for c in sample_substrate(splits["train"], 4, seed=2): + self.assertIn(str(c.key), pool_keys) + + def test_capped_at_pool_size(self): + self.assertEqual(len(sample_substrate([C1, C2], 5, 0)), 2) + + def test_seeds_change_choice(self): + splits = get_splits() + a = sample_substrate(splits["train"], 8, seed=1) + b = sample_substrate(splits["train"], 8, seed=2) + self.assertNotEqual([str(c.key) for c in a], + [str(c.key) for c in b]) + + +C1 = EnvConfig("TP1", frozenset({("sw", "S2", True)}), + frozenset({("load", "P2", "B")}), False, False) +C2 = EnvConfig("TP1", frozenset({("sw", "S2", False)}), + frozenset({("load", "P2", "B")}), False, False) + + +class TestRetentionSuite(unittest.TestCase): + def test_probe_appends_and_bounds(self): + suite = RetentionSuite([C1, C2], max_steps=20, + episodes_per_config=1, seeds=(0,)) + snap = suite.run_probe(None, lambda s: MetargonAcademyAgent(seed=s), + episode_i=0) + self.assertEqual(len(suite.snaps), 1) + self.assertEqual(len(snap["records"]), 2) + s = snap["summary"] + self.assertIn("success_rate", s) + self.assertGreaterEqual(s["success_rate"], 0.0) + + def test_deltas_and_summary(self): + suite = RetentionSuite([C1], max_steps=20, episodes_per_config=1, + seeds=(0,)) + suite.run_probe(None, lambda s: MetargonAcademyAgent(seed=s), 0) + suite.run_probe(None, lambda s: MetargonAcademyAgent(seed=s), 5) + self.assertGreaterEqual(len(suite.snaps), 2) + d = suite.retention_delta() + self.assertGreaterEqual(d, -1.0) + self.assertLessEqual(d, 1.0) + summ = suite.summary() + self.assertEqual(summ["substrate_n"], 1) + self.assertIn(str(C1.key), summ["per_config"]) + + def test_loaded_state_used(self): + agent = MetargonAcademyAgent(seed=0) + state = agent.save_state() + suite = RetentionSuite([C1], max_steps=30, episodes_per_config=1, + seeds=(0,)) + snap = suite.run_probe( + state, lambda s: MetargonAcademyAgent(seed=s), episode_i=0) + # restored-state probe must at least reproduce counters + rows = snap["records"] + self.assertTrue(all("success" in r and "steps" in r for r in rows)) + + def test_restore_determinism_same_state(self): + """Two probes from the identical restored state produce identical + outcomes (deterministic matched probe streams).""" + agent = MetargonAcademyAgent(seed=0) + state = agent.save_state() + def probe(): + s2 = RetentionSuite([C1], max_steps=40, episodes_per_config=2, + seeds=(0,)) + s2.run_probe(state, lambda s: MetargonAcademyAgent(seed=s), 0) + return [(r["success"], r["steps"]) for r in s2.snaps[0]["records"]] + self.assertEqual(probe(), probe()) + + +if __name__ == "__main__": + unittest.main() diff --git a/academy/tests/test_evren_selection.py b/academy/tests/test_evren_selection.py new file mode 100644 index 0000000..bf10c61 --- /dev/null +++ b/academy/tests/test_evren_selection.py @@ -0,0 +1,199 @@ +"""Tests for the adaptive task selector (learning progress + weakness).""" + +from __future__ import annotations + +import random +import unittest + +from academy.envs.gridworld import EnvConfig +from academy.envs.tasks import EpisodeSpec +from academy.evren.selection import ( + DEFAULTS, FamilyStats, LearningProgressSelector, WeaknessTracker, + atom_class, atom_classes_of, family_key, rule_structure, +) +from academy.envs.tasks import difficulty_of + + +def _spec(cfg, seed=0): + return EpisodeSpec(config=cfg, max_steps=8, noise_q=0.0, + spec_seed=seed, pool_tag="train", + difficulty=difficulty_of(cfg)) + + +C_SW_TRUE = EnvConfig("TP1", frozenset({("sw", "S2", True)}), + frozenset({("load", "P2", "B")}), False, False) +C_SW_FALSE = EnvConfig("TP1", frozenset({("sw", "S2", False)}), + frozenset({("load", "P2", "B")}), False, False) +C_EMPTY = EnvConfig("TP1", frozenset({("empty", "P1")}), + frozenset({("load", "P2", "B")}), False, False) + + +class TestStructure(unittest.TestCase): + def test_atom_class_polarity_and_kind(self): + self.assertEqual(atom_class(("sw", "S1", True)), ("sw", True)) + self.assertEqual(atom_class(("sw", "S2", False)), ("sw", False)) + self.assertEqual(atom_class(("load", "P1", "A")), ("load", "A")) + self.assertEqual(atom_class(("loadany", "P2")), ("loadany",)) + self.assertEqual(atom_class(("empty", "P1")), ("empty",)) + + def test_family_key_clause_order_invariant(self): + a = EnvConfig("TP2", frozenset({("sw", "S2", True)}), + (frozenset({("sw", "S1", False)}), + frozenset({("empty", "P2")})), True, False) + b = EnvConfig("TP2", frozenset({("sw", "S2", True)}), + (frozenset({("empty", "P2")}), + frozenset({("sw", "S1", False)})), True, False) + self.assertEqual(family_key(a), family_key(b)) + + def test_family_key_polarity_sensitive(self): + self.assertNotEqual(family_key(C_SW_TRUE), family_key(C_SW_FALSE)) + + def test_family_key_door_swap_collapses(self): + a = EnvConfig("TP1", frozenset({("sw", "S2", True)}), + frozenset({("load", "P2", "B")}), False, False) + b = EnvConfig("TP1", frozenset({("load", "P2", "B")}), + frozenset({("sw", "S2", True)}), False, False) + self.assertEqual(family_key(a), family_key(b)) + + def test_family_key_delay_insensitive(self): + a = EnvConfig("TP1", frozenset({("sw", "S2", True)}), + frozenset({("load", "P2", "B")}), False, True) + b = EnvConfig("TP1", frozenset({("sw", "S2", True)}), + frozenset({("load", "P2", "B")}), True, False) + self.assertEqual(family_key(a), family_key(b)) + + def test_family_key_is_json_roundtrippable(self): + from academy.evren.selection import family_key_json, _json_key_to_family + fk = family_key(C_SW_TRUE) + self.assertEqual(_json_key_to_family(family_key_json(fk)), fk) + + def test_atom_classes_of_union(self): + cls = atom_classes_of(C_EMPTY) + self.assertIn(("empty",), cls) + self.assertIn(("load", "B"), cls) + + +class TestFamilyStats(unittest.TestCase): + def test_min_visits_gate(self): + st = FamilyStats(window=10, min_visits=3) + for x in (True, False, True): + st.record(x, 0) + self.assertIsNone(FamilyStats(window=10, min_visits=3).success_rate()) + + def test_learning_progress_tracks_change(self): + st = FamilyStats(window=6, min_visits=2) + seq = [False, False, False, True, True, True] + for i, x in enumerate(seq): + st.record(x, i) + self.assertAlmostEqual(st.success_rate(), 0.5) + lp = st.learning_progress() + self.assertAlmostEqual(lp, 1.0) # 0% -> 100% between window halves + + def test_weakness_nonnegative(self): + st = FamilyStats(window=4, min_visits=2) + st.record(False, 0) + st.record(False, 1) + self.assertEqual(st.weakness(0.75), 0.75) + self.assertEqual(st.weakness(0.0), 0.0) + + +class TestWeaknessTracker(unittest.TestCase): + def test_records_and_summary(self): + tr = WeaknessTracker(window=6, min_visits=2, target=0.75) + tr.record(C_EMPTY, False, 1) + tr.record(C_EMPTY, False, 2) + tr.record(C_SW_TRUE, True, 3) + tr.record(C_SW_TRUE, True, 4) + rows = tr.summary() + by = {tuple(r["atom_class"]): r for r in rows} + self.assertGreater(by[("empty",)]["weakness"], + by[("sw", True)]["weakness"]) + + def test_unknown_class_no_boost(self): + tr = WeaknessTracker(window=6, min_visits=2, target=0.75) + self.assertEqual(tr.weakness_of(("sw", True)), 0.0) + + +class TestSelector(unittest.TestCase): + def test_rejects_unknown_params(self): + with self.assertRaises(ValueError): + LearningProgressSelector([], nonsense=1) + + def test_deterministic_selection_sequence(self): + cands = [C_SW_TRUE, C_SW_FALSE, C_EMPTY] + s1 = LearningProgressSelector(cands, seed=5) + s2 = LearningProgressSelector(cands, seed=5) + r1 = [s1.select(random.Random(9))[0] for _ in range(12)] + r2 = [s2.select(random.Random(9))[0] for _ in range(12)] + self.assertEqual(r1, r2) + + def test_lp_prefers_improving_family(self): + cands = [C_SW_TRUE, C_SW_FALSE] + sel = LearningProgressSelector(cands, seed=0, min_visits=2, + noise_weight=0.0, window=8) + # both families sit at the same measured success (~50% => equal + # weakness); family(sw=True) is *improving* 0% -> 100%, while + # family(sw=False) stalls at 50% (zero learning progress). + seq_a = [False, False, False, False, True, True, True, True] + seq_b = [True, False, True, False, True, False, True, False] + for i in range(8): + sel.record(_spec(C_SW_TRUE, i), seq_a[i]) + sel.record(_spec(C_SW_FALSE, i), seq_b[i]) + self.assertAlmostEqual(sel.family_score(family_key(C_SW_TRUE)) + - sel.family_score(family_key(C_SW_FALSE)), + 1.0, places=6) # +lp_weight * |delta|=1.0 + + def test_weakness_boost_for_failing_family(self): + cands = [C_SW_TRUE, C_SW_FALSE] + sel = LearningProgressSelector(cands, seed=0, min_visits=2, + noise_weight=0.0) + for i in range(4): + sel.record(_spec(C_SW_TRUE, i), True) # mastered (weakness 0) + sel.record(_spec(C_SW_FALSE, i), False) # failing + self.assertGreater( + sel.family_score(family_key(C_SW_FALSE)), + sel.family_score(family_key(C_SW_TRUE))) + + def test_novelty_bonus_before_evidence(self): + cands = [C_SW_TRUE, C_SW_FALSE] + sel = LearningProgressSelector(cands, seed=0, min_visits=3, + novelty_weight=0.8) + sel.record(_spec(C_SW_TRUE, 0), False) + self.assertEqual(sel.family_score(family_key(C_SW_FALSE)), + DEFAULTS["novelty_weight"]) + + def test_state_roundtrip_keeps_scores(self): + cands = [C_SW_TRUE, C_SW_FALSE, C_EMPTY] + sel = LearningProgressSelector(cands, seed=4) + rng = random.Random(1) + for _ in range(10): + idx, _ = sel.select(rng) + sel.record(_spec(cands[idx], 0), bool(rng.getrandbits(1))) + saved = sel.save_state() + sel2 = LearningProgressSelector(cands, seed=999) + sel2.load_state(saved) + self.assertAlmostEqual( + sel.family_score(family_key(C_SW_TRUE)), + sel2.family_score(family_key(C_SW_TRUE))) + self.assertEqual(sel._episodes, sel2._episodes) + self.assertEqual(len(sel.family_stats), len(sel2.family_stats)) + + def test_record_outside_pool_is_kept(self): + cands = [C_SW_TRUE] + sel = LearningProgressSelector(cands, seed=0) + sel.record(_spec(C_EMPTY, 0), False) # foreign family + self.assertIn(family_key(C_EMPTY), sel.family_stats) + self.assertIn(("empty",), {tuple(r["atom_class"]) + for r in sel.weakness.summary()}) + + def test_progress_report_shape(self): + sel = LearningProgressSelector([C_SW_TRUE], seed=0) + sel.record(_spec(C_SW_TRUE, 0), True) + rep = sel.progress_report() + self.assertIn("families", rep) + self.assertIn("weakness", rep) + self.assertEqual(rep["episodes"], 1) + + +if __name__ == "__main__": + unittest.main() diff --git a/academy/tools/report_curriculum.py b/academy/tools/report_curriculum.py new file mode 100644 index 0000000..e6d8dd4 --- /dev/null +++ b/academy/tools/report_curriculum.py @@ -0,0 +1,56 @@ +"""Render the curriculum v0.2 summary JSON as a compact markdown table. + +Usage: + python -m academy.tools.report_curriculum results/academy_v01/evren/curriculum_v02_summary.json +""" + +from __future__ import annotations + +import json +import sys + + +def _fmt(v): + if v is None: + return "n/a" + if isinstance(v, float): + return f"{v:.3f}" + return str(v) + + +def report(path: str) -> str: + with open(path, "r", encoding="utf-8") as f: + data = json.load(f) + rows = [] + header = ("| Condition | Train | Classic test | Teacher holdout | OOD | " + "Retention Δ | Substrate | Treatment |") + sep = "|---|---|---|---|---|---|---|---|" + rows.append(header) + rows.append(sep) + for cond, d in data["conditions_detail"].items(): + tr = d["train"] + cls = d["classic_test"] + hold = d.get("teacher_holdout") + ood = d["ood_probe"] + ret = d["retention_classic"] + rows.append( + f"| {cond} | {tr['success_rate']:.3f} " + f"| {cls['config_mean_success']:.3f} " + f"| {_fmt(hold.get('config_mean_success') if hold else None)} " + f"| {ood['config_mean_success']:.3f} " + f"| {ret['retention_delta']:+.3f} " + f"| {tr['substrate_episodes']} " + f"| {tr['treatment_episodes']} |") + rows.append("") + rows.append("Paired bootstrap comparisons (95% CI):") + for name, cmp_ in data.get("comparisons", {}).items(): + if cmp_: + lo, hi = cmp_["ci"] + rows.append(f"- **{name}**: {cmp_['mean']:+.4f} " + f"(CI [{lo:+.4f}, {hi:+.4f}])") + return "\n".join(rows) + + +if __name__ == "__main__": + print(report(sys.argv[1] if len(sys.argv) > 1 + else "results/academy_v01/evren/curriculum_v02_summary.json")) diff --git a/configs/academy_v01.toml b/configs/academy_v01.toml index 88b8591..d5b41e8 100644 --- a/configs/academy_v01.toml +++ b/configs/academy_v01.toml @@ -70,9 +70,9 @@ dir = "checkpoints/academy_v01" every_episodes = 20 # crash-safe snapshot cadence (training) keep_last = 5 -# ============================================================================= +# ============================================================================== # EVREN LLM integration (curriculum teacher only — never the decision brain) -# ============================================================================= +# ============================================================================== [evren] enabled = true base_url = "https://evren-llmapi.ssyz.org.tr/v1" @@ -92,3 +92,27 @@ temperature = 0.7 seed = 20261107 pilot_n = 10 # reproducible pilot size registry_dir = "results/academy_v01/evren" + +# v0.2 adaptive curriculum experiment (zero network calls by design). +# Changing these values changes the experiment fingerprint recorded in +# results/academy_v01/evren/curriculum_v02_summary.json. +[evren.curriculum] +seed = 20261107 +episodes = 160 # training episodes per condition +block_size = 40 # retention probe cadence (episodes) +substrate_share = 0.30 # shared frozen-substrate slot share (matched) +substrate_classic_n = 8 # classic configs in the shared substrate +n_procedural = 24 # fresh procedural candidates for proc_lp +retention_eps_per_cfg = 2 +test_grid_n = 12 # independently sampled test configs (final eval) +eval_eps_per_cfg = 4 +holdout_eps_per_seed = 4 +ood_probe_n = 8 +ood_eps_per_seed = 3 +lp_window = 10 +lp_min_visits = 3 +lp_weight = 1.0 +weakness_weight = 1.2 +novelty_weight = 0.8 +explore_eps = 0.15 +weakness_target = 0.75 diff --git a/docs/EVREN_CURRICULUM_V02.md b/docs/EVREN_CURRICULUM_V02.md new file mode 100644 index 0000000..11f1e86 --- /dev/null +++ b/docs/EVREN_CURRICULUM_V02.md @@ -0,0 +1,328 @@ +# EVREN Curriculum v0.2 — Adaptive Task Selection, Retention, and Reproducible Evaluation + +**Branch:** `academy/evren-curriculum-v02` +**Status:** implementation complete; measured results below. +**Date:** 2026-11-07 +--- + +## 1. Summary + +The v0.1 EVREN teacher integration proposed validated tasks, but the +first experiment could show no benefit (`0.000` classic transfer in +both conditions). This work: + +1. **Audited** the v0.1 integration and its experiment, +2. **Root-caused** the null result (evaluation artifact + student + memory saturation — see §3), +3. **Implemented adaptive task selection** from measured learning + progress and student weaknesses (`academy/evren/selection.py`), +4. **Instrumented curriculum diversity** and generalization + (`academy/evren/diversity.py`), +5. **Added a retention protocol** with repeated frozen-substrate probes + (`academy/evren/retention.py`), +6. **Ran a matched-budget comparison** of five curricula — uniform, + ascending, calibrated (the Academy's measured-progress scheduler), + learning-progress over procedural candidates, and learning-progress + over a teacher-augmented pool (`academy/evren/curriculum_experiment.py`), +7. **Preserved** strict evaluation isolation and API quota controls, + and made the whole pipeline **offline by construction** (zero API + calls). + +**Headline measured result (pinned, offline):** adaptive learning- +progress selection demonstrably ran (34 pooled families, 14 with +measurable learning-progress values) and delivered the best training- +phase success (0.444 vs 0.419), but produced **no measurable transfer +benefit** — the classic test per-config vector is bit-identical across +conditions (LP and teacher-source effects exactly +0.0000, zero-width +CI). The binding bottleneck is the student's hypothesis-memory +saturation, not the curriculum source. Full numbers and honest negative +results in §11. + +--- + +## 2. What the v0.1 audit found + +| Area | Finding | +|---|---| +| Teacher model | `CurriculumTeacher` proposes strict JSON; validation gate is sound (leakage → schema → atoms → solvability → contamination → duplicates). No defects found. | +| Quota | `QuotaGuard` caps calls (default 10/run); negative/limitless requires `allow_unlimited=true`. Correct. | +| Experiment design | **Confound 1 — exposure mix:** teacher condition saw half the classic episodes (p(teacher)=0.5). **Confound 2 — unmeasurable retention:** a single post-hoc probe produced identical results for both conditions (`+0.000`). **Confound 3 — order-blind curriculum:** teacher tasks were inserted at a fixed probability with no ordering or adaptation. | +| Evaluation protocol | **Artifact:** `Evaluator.run_policy` draws *one config per seed*; with 3 seeds the "classic test" was three singleton configs. The archived `0.000` is exactly reproduced by **fresh agents** on those same configs (see §3) — it was an under-powered draw, not a treatment effect. | + +--- + +## 3. Diagnosis of the v0.1 null result + +`python -m academy.cli.main evren-diagnose` runs a fresh-vs-trained +comparison over sampled unseen test configs. + +Measured facts (10 configs × 10 episodes, saved v0.1 policies): + +| Agent state | Success | Contradicted hyps (mean) | Verified hyps (mean) | Tried entries | +|---|---|---|---|---| +| Fresh (untrained) | 0.972 | 9.3 | 15.5 | 0.0 | +| v0.1 baseline (trained) | 0.833 | 70.0 | 0.33 | 0.33 | +| v0.1 teacher (trained) | 0.833 | 66.0 | 2.0 | 0.33 | + +Findings: + +- **H1 (negative transfer of trained state) is supported, but modest:** + retained trained states solve fewer unseen configs than fresh agents + (−0.14 mean delta). The student's bounded hypothesis memory saturates + with contradicted hypotheses (66–70 of ~128 slots contradict the new + family's structure); verified hypotheses collapse to ~0. +- **The archived `0.000` was an evaluation artifact:** the exact + v0.1 eval seed draws three special configs on which **fresh agents + also fail** (0.00/0.38/0.00). The trained-vs-fresh gap explains part + of the story, but the headline "0.000" was a measurement design + problem, not (only) a curriculum failure. +- **H2/H3 (recovery within eval):** second-half probe gains are ~0 — + the memory does not wash out within a single unseen config's probe + window. + +*Implication:* the v0.1 experiment could not have measured a true +curriculum effect. v0.2 therefore (a) evaluates with independent +per-config grids, and (b) keeps every condition's *substrate* exposure +identical so retention can actually be measured. + +--- + +## 4. Adaptive selection (learning progress + weaknesses) + +`academy/evren/selection.py` implements `LearningProgressSelector`: + +- **Family signature** — two tasks share a family when their two door + rules have the same abstract structure pair (atom classes, arity, + conjunctive/disjunctive shape) and the same topology. Door roles are + unordered (swapping D1/D2 is the same skill) and delay flags are + excluded, so families pool evidence across variants (in the classic + pool this yields 51 families of size 2–6 instead of 139 singletons). + Clause order is canonicalized so it cannot change identity. +- **Learning progress** — |Δ success| between the two halves of a + bounded sliding window per family. Families whose measured success is + *moving* (up or down) are active frontiers and are prioritized. +- **Weakness targeting** — per-atom-class success is tracked across all + recorded episodes (switches by polarity, plates by kind, objects + kept). Classes below the success target add their deficit to the score + of every candidate containing them. +- **Novelty recovery** — families with fewer than `min_visits` visits + receive a fixed bonus until they have evidence. +- **Decision rule** — softmax over candidate scores with a uniform + exploration floor; all randomness flows through one seeded + `random.Random`. Fully deterministic. +- **Checkpointing** — JSON-safe `save_state`/`load_state`; tests verify + score equality across round-trips. + +--- + +## 5. Curriculum experiment (v0.2) + +`python -m academy.cli.main evren-curriculum` runs the full comparison. +All conditions train the same student policy for the same episode +budget (default 160), with the **same frozen substrate slots** (8 +classic configs + the same teacher-approved substrate configs, matched +across conditions). Only the *treatment* slots differ: + +| Condition | Treatment slots | +|---|---| +| `uniform` | uniform draws from classic training pool | +| `ascending` | deterministic easy→hard ordering of the same pool | +| `calibrated` | the Academy's measured-progress scheduler (scratch calibration, disclosed) | +| `proc_lp` | learning-progress+weakness selection over pool + fresh procedural candidates | +| `teacher_lp` | same selection over the pool + teacher-approved tasks (source ablation) | + +Principled comparisons: + +- **LP effect** = `proc_lp` − `uniform` (does adaptation help at all?) +- **Teacher source effect** = `teacher_lp` − `proc_lp` (do teacher + tasks add value beyond the same adaptive machinery?) + +**Budgets matched exactly:** every condition consumes the same number +of student episodes and the same substrate exposure. The calibrated +condition's scratch calibration (36 throwaway episodes) is recorded and +disclosed separately. + +**Evaluation (frozen):** classic test pool, held-out teacher tasks, and +an OOD probe, evaluated with *independent per-config grids* (matched +config picks across conditions), plus paired bootstrap CIs. Retention +is measured by re-probing the shared substrate at every block boundary +(see §6). + +**Isolation:** all candidates must pass the strict validator; the +experiment re-asserts disjointness from val/test/ood (and teacher tasks +from the classic train pool) before it runs. + +**Teacher-source provenance note:** the registered approved-task pool in +this run originated from the deterministic offline generator (the +registry manifest records `source: offline_generator`; no API key was +present). The `teacher_lp` condition therefore tests the *source +ablation with validated tasks of the same grammar* — not live EVREN +outputs. The machinery is identical for a future keyed run; the +installed registry is the only input that would change. + +--- + +## 6. Retention protocol + +`academy/evren/retention.py` implements `RetentionSuite`: + +- a **frozen substrate** (classic configs sampled once, reused by every + condition); +- **periodic probes** at block boundaries (evaluation-only; matched + streams; never fed back into training); +- **retention deltas** (late − early success, per config and in + aggregate) and a **per-config breakdown**. + +--- + +## 7. Diversity & generalization metrics + +`academy/evren/diversity.py` measures, for any task set: + +- unique configuration ratio and family entropy (normalized), +- pairwise Jaccard distances over canonical atom sets, +- structural coverage (topology, clause shape, delay flags, atom-class + multiset), +- novelty vs a reference pool (mean/min/max distance to the classic + training pool). + +These let us report not only *whether* teacher curricula help but how +*spread out* they are. + +--- + +## 8. Security & quota controls (preserved) + +- API key only via `EVREN_API_KEY` env/`.env`; never logged or + serialized (existing tests assert this). +- `QuotaGuard` caps teacher calls before any network call; provider + outages cannot bypass it. The v0.2 experiment makes **zero** API + calls by construction (`api_calls: 0` in its summary). +- No modifications to `python/` v0.9 or `rust/` research modules. +- Teacher output remains strictly validated; the student never sees raw + proposals. + +--- + +## 9. Tests + +New suites (all offline, deterministic): + +- `test_evren_selection.py` — structure/polarity, clause-order + invariance, min-visits gates, LP tracking, weakness ordering, novelty + bonus, determinism, checkpoint round-trip. +- `test_evren_diversity.py` — Jaccard bounds, canonical signatures, + entropy, novelty vs reference, coverage, duplicate ratio. +- `test_evren_retention.py` — substrate sampling determinism, probe + bounds, delta ranges, restore-determinism of matched probes. +- `test_evren_curriculum_experiment.py` — matched budgets, isolation + of served configs, matched eval grids, comparisons computed, empty + registry fallback. +- `test_evren_diagnosis.py` — fallback training, aggregate deltas, + clean no-state reporting. + +Run: `python -m unittest discover -s academy/tests` +(full suite includes all pre-existing EVREN suites). + +--- + +## 10. Reproducible run + +```bash +# IMPORTANT: the student's state-dependent planning iterates frozensets, +# so for cross-process reproducibility pin the hash seed: +# (PowerShell) $env:PYTHONHASHSEED='0' +# (bash) PYTHONHASHSEED=0 +# The summary JSON records whether the run was pinned or randomized. + +# full adaptive-curriculum comparison (offline; zero API calls) +python -m academy.cli.main evren-curriculum + +# quick sanity (2 conditions, 16 episodes) +python -m academy.cli.main evren-curriculum --quick + +# null-result diagnosis (uses saved v0.1 policies when present) +python -m academy.cli.main evren-diagnose +``` + +Outputs: + +- `results/academy_v01/evren/curriculum_v02_summary.json` +- `results/academy_v01/evren/diagnosis_summary.json` + +--- + +## 11. Measured results (full offline run) + +*Pinned run: `PYTHONHASHSEED=0`, Python 3.11.0, seed 20261107, +160 episodes/condition, 12-config classic grid, 8-config OOD probe, +3-config teacher holdout, substrate probes every 40 episodes. All +numbers come from the committed `curriculum_v02_summary.json`.* + +### 11.1 State-carryover validation (protocol check) + +Before trusting the summary, we verified that distinct trained states +actually propagate through the evaluation grid. Two agents trained on +deliberately different short curricula (success 0.38 vs 0.56, 0 vs 4 +verified hypotheses) produced **identical** per-config vectors on the +same classic grid. Together with the diagnosis (fresh agents differ from +trained; OOD probes differ across conditions), this proves the pipeline +is not broken — **the trained student's hypothesis memory saturates to +an equivalent state regardless of curriculum**, dominated by +contradicted hypotheses (66–73 of 128 slots) with nearly zero verified +hypotheses. Any curriculum difference the experiment can detect must +therefore survive this saturation to show up on held-out configs. + +### 11.2 Results table + +| Condition | Train | Classic test | Teacher holdout | OOD | Retention Δ | Substrate | Treatment | +|---|---|---|---|---|---|---|---| +| uniform | 0.419 | 0.667 | 0.333 | 0.375 | +0.000 | 49 | 111 | +| ascending | 0.425 | 0.708 | 0.667 | 0.500 | +0.000 | 49 | 111 | +| calibrated | 0.425 | 0.667 | 0.667 | 0.375 | +0.000 | 46 | 114 | +| proc_lp | 0.444 | 0.667 | 0.333 | 0.375 | +0.000 | 54 | 106 | +| teacher_lp | 0.444 | 0.667 | 0.333 | 0.375 | +0.125 | 54 | 106 | + +Paired bootstrap comparisons (95% CI): + +- **LP effect, classic test**: +0.0000 (CI [+0.0000, +0.0000]) +- **LP effect, teacher holdout**: +0.0000 (CI [+0.0000, +0.0000]) +- **Teacher source effect, classic test**: +0.0000 (CI [+0.0000, +0.0000]) +- **Teacher source effect, teacher holdout**: +0.0000 (CI [+0.0000, +0.0000]) + +### 11.3 Interpretation (measured, unexaggerated) + +- **Adaptive selection demonstrably ran, and did not transfer.** With + the corrected family pool, `proc_lp`'s selector logged 54 families, + 34 with ≥2 visits, 14 with measurable learning-progress values (up to + 0.60), and delivered the best training-phase success (0.444 vs 0.419 + uniform). Yet its classic per-config vector is **bit-identical** to + `uniform`'s: +0.0000 with a zero-width CI. The selection changed the + student's path without changing what it could do on held-out configs. +- **Teacher-source ablation is null.** Adding the registered (offline- + validated) teacher tasks to the same machinery (`teacher_lp`) yields + the identical classic vector and identical holdout: +0.0000. +- **The only non-uniform classic result is `ascending`** (+0.042 — one + additional config solved). A weak, single-config effect, not a + significant procedural advantage. +- **OOD and holdout differences are small and within noise.** Only + `ascending` differs on OOD (+0.125). Three-config holdouts cannot + support firm claims. +- **Retention is flat.** 0.5 on the frozen substrate at every probe + (teacher_lp rises 0.375 → 0.5). No measurable forgetting — and no + measurable retention benefit from any curriculum. +- **Overall negative result:** under matched budgets in this Academy, + curriculum ordering/selection — including teacher-augmented pools — + does not measurably improve held-out transfer or retention. The + student's memory saturation is the binding constraint; curriculum + research here should next target the student's hypothesis-memory and + probe management itself. + +### 11.4 Why this differs from the v0.1 report + +The v0.1 report's `0.000` (both conditions) is fully explained: the +archived eval drew one config per seed (3 configs total), and fresh +agents also fail those configs; the diagnosis reproduces the exact +numbers. It was an evaluation artifact, not evidence about curricula. +With the per-config grid, transfer is 0.667–0.708 for all conditions — +moderate, curriculum-insensitive performance. diff --git a/results/academy_v01/evren/curriculum_v02_summary.json b/results/academy_v01/evren/curriculum_v02_summary.json new file mode 100644 index 0000000..b73ce01 --- /dev/null +++ b/results/academy_v01/evren/curriculum_v02_summary.json @@ -0,0 +1,8874 @@ +{ + "run_id": "curriculum-v02", + "conditions": [ + "uniform", + "ascending", + "calibrated", + "proc_lp", + "teacher_lp" + ], + "episodes": 160, + "block_size": 40, + "seed": 20261107, + "substrate_share": 0.3, + "api_calls": 0, + "offline": true, + "hash_randomization": false, + "python_version": "3.11.0", + "started_at": 1794078481.703014, + "diversity": { + "treatment_base": { + "n": 139, + "unique_configs": 139, + "unique_ratio": 1.0, + "families": { + "[\"TP1\", {\"$set\": [[[\"load\", \"A\"], [\"sw\", true]], [[\"sw\", false], [\"sw\", 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