An introductory guide to reinforcement learning using environments from the OpenAI Gymnasium Python package. It covers high-level concepts like Q-learning, Markov Decision Processes, state-value vs. action-value, and the balance between exploration and exploitation. Practical examples, such as navigating a frozen lake, are used to illustrate these concepts.
Table of contents
An Intuitive Introduction to Reinforcement Learning, Part IThe Agent, State, and EnvironmentMarkov Decision Processes (MDPs)Frozen Lake ProblemExploration vs. ExploitationSummaryReferencesSort: