Multi-Agent Deep Reinforcement Learning in 13 Lines of Code Using PettingZoo

This tutorial provides a simple introduction to using multi-agent reinforcement learning, assuming a little experience in machine learning and knowledge of Python. Reinforcement stems from using machine learning to optimally control an agent in an environment. It works by learning a policy, a function that maps an observation obtained from its environment to an action. Policy functions are typically deep neural networks, which gives rise to the name "deep reinforcement learning." The goal of reinforcement learning is to learn an optimal policy, a policy that achieves the maximum expected reward from the environment when acting.