Info

This is an implementation of a DQN Network to play chess vs:

• Baseline
• Itself
• Stockfish

Pickels are made from PGN games. Buckets are made from pickels. DQN train on pickles.

General

Set up env:

make pyenv make install

Gitignore:

Your data in

• raw_data/
• data/
• weights/ Your search notebooks in research/

Data

Working with: https://database.lichess.org/standard/lichess_db_standard_rated_2015-07.pgn.bz2

Boilerplate README

This is a boilerplate repo for a reinforcement learning (RL) project.

This directory provides an example repository structure for RL projects using pytorch. This template provides a generic agent using the deep Q-learning algorithm as well as an agent playing random actions for baseline performance. The DQN architecture is in itw own class and is hot-swappable with other potential architectures. A sample environment using OpenAi's gym and a generic control loop is also provided.

Note that since RL projects are rarely data-centric, and data has to be generated on-the-fly, requirements are likely to differ from standard ML projects.

Detailed package workflow

This boilerplate package contains multiple modules:

• main.py is the entry point of the package. It defines the agent and environment to use.
• environment.py defines environment-side setup and execution utilities. It uses the gym package for demonstration purposes.
• agent.py defines multiple types of learning agent. We have included a random agent and deep Q-learning agent for demonstration purposes.
• config.py defines a singleton class used for storing simulation parameters. This class is globally available in all packages (through the CFG variable). It has to be initialized once (see module documentation).
• network.py defines the neural network used by the DQN agent.