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Explore fundamental concepts of Deep Reinforcement Learning in this comprehensive lecture that delves into the core principles of Reinforcement Learning, including Policy iteration and value iteration methodologies. Master the theoretical foundations of Markov decision process (MPD), understand the implementation of Monte Carlo estimation techniques, and learn about temporal difference algorithms. Through this 100-minute session, gain essential knowledge that serves as a crucial foundation for advanced applications in Deep Reinforcement Learning and artificial intelligence systems.

Deep Learning: Introduction to Reinforcement Learning and Policy Iteration - Lecture 12

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#Computer Science #Machine Learning #Reinforcement Learning #Deep Learning #Markov Decision Processes #Mathematics #Applied Mathematics #Monte Carlo Methods

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Deep Learning: Introduction to Reinforcement Learning and Policy Iteration - Lecture 12

Ali Ghodsi, Deep Learning, Deep Reinforcement Learning-Part 1, Deep RL, Fall 2023, Lecture 12