Explore Bayesian Reinforcement Learning in this comprehensive lecture covering key concepts such as inaccurate models, planning, sampling, value iteration, posterior belief, and the exploration-exploitation tradeoff. Delve into both offline and online RL approaches while gaining insights into the benefits of Bayesian methods in reinforcement learning. Learn from Pascal Poupart as he provides a thorough introduction and recap of RL fundamentals before diving deep into Bayesian RL techniques.