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Reinforcement Learning 1: Introduction to Reinforcement Learning

Reinforcement Learning 2: Exploration and Exploitation

Reinforcement Learning 3: Markov Decision Processes and Dynamic Programming

Reinforcement Learning 4: Model-Free Prediction and Control

Reinforcement Learning 5: Function Approximation and Deep Reinforcement Learning

Reinforcement Learning 6: Policy Gradients and Actor Critics

Reinforcement Learning 7: Planning and Models

Reinforcement Learning 8: Advanced Topics in Deep RL

Reinforcement Learning 9: A Brief Tour of Deep RL Agents

Reinforcement Learning 10: Classic Games Case Study

Description:

Embark on a comprehensive 17-hour journey into the world of Reinforcement Learning. Explore key concepts from introduction to advanced topics, covering exploration and exploitation, Markov Decision Processes, dynamic programming, model-free prediction and control, function approximation, deep reinforcement learning, policy gradients, actor critics, planning, and models. Gain practical insights through a case study on classic games and take a tour of deep RL agents. Master the fundamentals and cutting-edge techniques of this powerful machine learning paradigm.

Reinforcement Learning Course

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#Computer Science #Machine Learning #Reinforcement Learning #Markov Decision Processes #Algorithms #Dynamic programming #Function Approximation #Deep Reinforcement Learning #Policy Gradient

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