Exploration of three key reinforcement learning algorithms: Dynamic Programming (DP) for optimal policies in MDPs, Monte Carlo methods for learning from complete episodes without a model, and Temporal Difference (TD) learning for efficient updates from incomplete episodes using bootstrapping. Each method has unique characteristics and trade-offs essential for understanding advanced concepts in reinforcement learning.
A Markov Decision Process (MDP) is a mathematical framework used for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision-maker.
