Control Systems and Reinforcement Learning

Control Systems and Reinforcement Learning

Meyn, Sean

Cambridge University Press

06/2022

450

Dura

Inglês

9781316511961

15 a 20 dias

1040

Descrição não disponível.
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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