Completed as part of an Imperial College Coursework in Reinforcement Learning (CO424).

Base Files:

• Coursework_Part_1.pdf containing all the main coursework instructions and questions.
• Tutorial.pdf which explains how to implement Deep Q-Learning through several stages. It is aligned with Coursework_Part_1.pdf.
• starter_code.py providing Python 3 code which you will build upon during this tutorial and the associated coursework.
• environment.py in which the environment is implemented. This file should not be modified.
• torch_example.py which gives an example of a supervised learning experiment in PyTorch (see section 2 in Tutorial.pdf for more information).

Requirements

pip install -r requirements.txt

This will install the following libraries (and their dependencies):

- ```torch``` 
- ```opencv-python```
- ```numpy```
- ```matplotlib```

## How to run a script ?

```shell script
python torch_example.py  # To launch the pytorch example script
python starter_code.py  # To launch the coursework script

Techniques used:

Double Deep Q-Learning

Custom Experience Replay Buffer to sample uniformly across map areas

Free exploration before training

Continuous action space using Cross-Entropy Method