Control Systems and Reinforcement Learning
portes grátis
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.
- 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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
- 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.
- 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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
