The book is written for newcomers to reinforcement learning who wish to write code for various applications, from robotics to power systems to supply chains. It also contains advanced material designed to prepare graduate students and professionals for both research and application of reinforcement learning and optimal control techniques.
The book is written for newcomers to reinforcement learning who wish to write code for various applications, from robotics to power systems to supply chains. It also contains advanced material designed to prepare graduate students and professionals for both research and application of reinforcement learning and optimal control techniques.
Sean Meyn is a professor and holds the Robert C. Pittman Eminent Scholar Chair in the Department of Electrical and Computer Engineering, University of Florida. He is well known for his research on stochastic processes and their applications. His award-winning monograph Markov Chains and Stochastic Stability with R. L. Tweedie is now a standard reference. In 2015 he and Prof. Ana Busic received a Google Research Award recognizing research on renewable energy integration. He is an IEEE Fellow and IEEE Control Systems Society distinguished lecturer on topics related to both reinforcement learning and energy systems.
Inhaltsangabe
1. Introduction Part I. Fundamentals Without Noise: 2. Control crash course 3. Optimal control 4. ODE methods for algorithm design 5. Value function approximations Part II. Reinforcement Learning and Stochastic Control: 6. Markov chains 7. Stochastic control 8. Stochastic approximation 9. Temporal difference methods 10. Setting the stage, return of the actors A. Mathematical background B. Markov decision processes C. Partial observations and belief states References Glossary of Symbols and Acronyms Index.
1. Introduction Part I. Fundamentals Without Noise: 2. Control crash course 3. Optimal control 4. ODE methods for algorithm design 5. Value function approximations Part II. Reinforcement Learning and Stochastic Control: 6. Markov chains 7. Stochastic control 8. Stochastic approximation 9. Temporal difference methods 10. Setting the stage, return of the actors A. Mathematical background B. Markov decision processes C. Partial observations and belief states References Glossary of Symbols and Acronyms Index.
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