Reinforcement Learning : An Introduction /
I tiakina i:
| Kaituhi matua: | |
|---|---|
| Ētahi atu kaituhi: | |
| Hōputu: | Tāhiko īPukapuka |
| Reo: | Ingarihi |
| I whakaputaina: |
Cambridge, Mass. :
MIT Press,
1998.
|
| Rangatū: | Book collections on Project MUSE.
|
| Ngā marau: | |
| Urunga tuihono: | Full text available: |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
Rārangi ihirangi:
- Contents
- Series Foreword
- Preface
- I. The Problem
- 1. Introduction
- 2. Evaluative Feedback
- 3. The Reinforcement Learning Problem
- II. Elementary Solution Methods
- 4. Dynamic Programming
- 5. Monte Carlo Methods
- 6. Temporal-Difference Learning
- III. A Unified View
- 7. Eligibility Traces
- 8. Generalization and Function Approximation
- 9. Planning and Learning
- 10. Dimensions of Reinforcement Learning
- 11. Case Studies
- References
- Summary of Notation
- Index.