Reinforcement learning : an introduction /
Guardado en:
| Autor principal: | |
|---|---|
| Otros Autores: | |
| Formato: | Libro |
| Lenguaje: | Inglés |
| Publicado: |
Cambridge, MA :
MIT Press,
c2018
|
| Edición: | 2nd ed. |
| Colección: | Adaptive computation and machine learning series
|
| Materias: | |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
Tabla de Contenidos:
- I
- Introduction 1.
- Tabular solution methods I
- Multi-armed bandits 2.
- Finite Markov decision processes 3.
- Dynamic programming 4.
- Monte Carlo methods 5.
- Temporal-difference learning 6.
- n-step bootstrapping 7.
- Planning and learning with tabular methods 8.
- Approximate solution methods II
- On-policy prediction with approximation 9
- On-policy control with approximation 10.
- *Off-policy methods with approximation 11.
- Eligibility traces 12.
- Policy gradient methods 13.
- Looking deeper III
- Psychology 14.
- Neuroscience 15.
- Applications and case studies 16.
- Frontiers 17.