Lectures related to Policy Estimation: The Log-Likelihood Trick

Explores advanced reinforcement learning topics, including policies, value functions, Bellman recursion, and on-policy TD control.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Introduces the basics of reinforcement learning, including Q-learning and epsilon-greedy policies.