Reinforcement Learning
Policy gradient, Q-learning, and actor-critic methods built from the Bellman equation up. Train agents in MuJoCo and Atari.
Intermediate14 weeks · 14 lessons
Your progress00 / 14 · 0%
Lessons
W01Week 1: Reinforcement Learning Problem FormulationGeneral→W02Week 2: Multi-Armed BanditsGeneral→W03Week 3: Dynamic Programming for Finite MDPsGeneral→W04Week 4: Monte Carlo and Temporal-Difference LearningGeneral→W05Week 5: Function Approximation in Reinforcement LearningGeneral→W06Week 6: Deep Q-Learning and VariantsGeneral→W07Week 7: Policy Gradient and Actor–Critic MethodsGeneral→W08Week 8: Modern Deep Reinforcement Learning AlgorithmsGeneral→W09Week 9: Exploration, Partial Observability, and Multi-Agent Reinforcement LearningGeneral→W10Week 10: Model-Based Reinforcement Learning and PlanningGeneral→W11Week 11: Offline Reinforcement LearningGeneral→W12Week 12: Reinforcement Learning from Human FeedbackGeneral→W13Week 13: Direct Preference Optimization and GRPOGeneral→W14Week 14: Agentic Systems and Course CapstoneGeneral→