Art of Reinforcement Learning

Part I: Foundation.- Chapter 1: Introduction to Reinforcement Learning.- Chapter 2: Markov Decision Processes.- Chapter 3: Dynamic Programming.- Chapter 4: Monte Carlo Methods.- Chapter 5: Temporal Difference Learning.- Part II: Value Function Approximation.- Chapter 6: Linear Value Function Approximation.- Chapter 7: Nonlinear Value Function Approximation.- Chapter 8: Improvement to DQN.- Part III: Policy Approximation.- Chapter 9: Policy Gradient Methods.- Chapter 10: Problems with Continuous Action Space.- Chapter 11: Advanced Policy Gradient Methods.- Part IV: Advanced Topics.- Chapter 12: Distributed Reinforcement Learning.- Chapter 13: Curiosity-Driven Exploration.- Chapter 14: Planning with a Model - AlphaZero.

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• Aprendizagem mecânica
• Inteligência artificial
• Programação e linguagens de script: geral

Reinforcement Learning;Machine Learning;Monte Carlo Methods;Value Function Approximation;Markov Decision Process;Q-learning;Python