Deep Reinforcement Learning

An advanced course examining reinforcement learning from Markov decision processes and multi-armed bandits to deep neural approaches. Students practice tabular methods such as Monte Carlo, temporal-difference learning, and Q-learning, then build deep Q-network, policy-gradient, and actor-critic agents, with attention to safety and ethical issues.