Rele-Zoo

This is a tool/reference repository of several reinforcement learning (RL) algorithms that I implemented for the main purpose of learning RL. I wanted to have a go-to catalog of RL algorithms and applications which also allowed me to reuse big parts of what implies to run an RL algorithm/task and quickly try out new ideas/algorithms.

Please check out the documentation page for more detailed usage.

System requirements

• Unix based systems
• Python >= 3.8
• Conda >= 4.x

Installation

Check-out the repository and run the following:

make install-env
make install
conda activate rele-zoo

Usage

Run tool

Run algorithms and tasks with relezoo-run. The tool is powered by hydra, by default, it will train a REINFORCE algorithm in the OpenAI Gym cartpole environment and generate some tensorflow logs in the generated output folder.

relezoo-run

Run the --help option to check the default values. You can change any configuration parameter as needed, for example, to run against Acrobot-v1 environment instead, just run the tool like this:

relezoo-run environments@env_train=acrobot

By default, relezoo-run runs in train mode, to run in test mode, specify the property and the path to the checkpoints as per chosen algorithm. For example to use the baselines within this repository:

relezoo-run context.mode=play context.epochs=5 context.render=true checkpoints=baselines/reinforce/cartpole/

Shell

For convenience, there is an iphyton shell wrapper where you can run Ad-Hoc experiments directly.

relezoo shell

References

The development of this project is inspired by:

• OpenAI Spinning Up
• Stable Baselines 3
• Deep Reinforcement Learning Hands-On