This repository contains a few RL agents as samples, in order to give you hints about how to implement a more involved MARLO agent (note that it's not mandatory to use ChainerRL to participate in the context, though).

Chainer "is a Python-based deep learning framework aiming at flexibility". It has a very powerful high-level API aimed at training deep learning networks and as such is very useful in a RL context. ChainerRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer.

The framework presents a wide range of algorithms and deep learning tools which facilitate a quick start-up and as such is ideal for drafts. ChainerRL communicates seamlessly with OpenAI's Gym framework, thus relieving a lot of structural stress off of you - the competitor - and allowing you to focus strictly on your agent's behaviour.

This section contains pointers to:

• Setup ChainerRL
• A step-by-step PPO Agent tutorial
• Experiments and plotting examples using TensorBoard