This paper provides an overview of reinforcement learning (RL), a type of machine learning where an agent learns to make decisions in an environment to maximize rewards. The agent interacts with the environment, takes actions, and receives rewards based on its actions. The goal of RL is to find the best policy, or set of rules, that guides the agent's actions to get the most rewards over time. The notebook discusses different types of RL problems, such as Markov Decision Processes (MDPs) and bandits, which are simplified models of the real world. It also covers various RL algorithms, like value-based methods (e.g., Q-learning) and policy gradient methods, which are used to learn the optimal policy. The notebook also explores advanced topics in RL, including model-based RL, where the agent learns a model of the environment to plan ahead, and exploration strategies, which help the agent discover new and potentially better actions.

https://arxiv.org/pdf/2412.05265