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Explore advanced reinforcement learning concepts through this comprehensive graduate-level course from the University of Waterloo's Computer Science department. Master the design of algorithms that enable machines to learn from partial, implicit, and delayed feedback rather than direct supervision. Delve into foundational topics including Markov decision processes, multi-armed bandits, and value iteration before progressing to cutting-edge areas like deep reinforcement learning, hierarchical reinforcement learning, and partially observable environments. Study both model-free and model-based approaches, policy gradient methods, actor-critic algorithms, and trust region optimization techniques. Examine practical applications through case studies in robotic control, autonomous vehicles, game playing (including Go, Chess, and FPS games), conversational agents, and visual navigation. Learn about specialized topics such as inverse reinforcement learning, memory-augmented networks, safe multi-agent systems, and lifelong learning in complex environments like Minecraft. Gain hands-on experience with modern tools including OpenAI environments, TensorFlow implementations, and deep Q-networks while understanding the theoretical foundations of Bayesian reinforcement learning, contextual bandits, and semi-Markov decision processes.
