Reinforcement Learning (RL) trains an agent to make decisions by interacting with an environment. Key concepts include states, actions, rewards, policies, and the Markov Decision Process (MDP). This post explains the basics of RL, discusses Q-Learning and other RL algorithms, and provides a Python implementation example using the FrozenLake environment.
Table of contents
Key Concepts in Reinforcement LearningMarkov Decision ProcessSteps of Reinforcement LearningReinforcement Learning AlgorithmsQ-Learning AlgorithmImplementation of Q-Learning with PythonConclusionDiscover How Machine Learning Algorithms Work!Sort: