An Introduction to Reinforcement Learning with OpenAI Gym, RLlib, and Google Colab

The CartPole environment is a classic reinforcement learning problem where an agent must balance a pole on top of a moving cart to maximize its total reward over time. The agent learns by interacting with an environment and receiving rewards or penalties for its actions, with the ultimate goal of balancing the pole successfully. Through various code examples and video demonstrations, this tutorial provides an introduction to reinforcement learning concepts, such as agents, environments, observations, actions, rewards, and policies. The tutorial also explores how a neural network can represent a policy in deep reinforcement learning, and how RLlib's Proximal Policy Optimization (PPO) algorithm can be used to train the agent. Additionally, the tutorial discusses hyperparameter tuning using Ray Tune, which allows users to find optimal hyperparameters for solving the CartPole environment in the fewest timesteps possible.

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