Adaptive Representations for Reinforcement Learning
This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own r...
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| Format: | Electronic |
| Language: | English |
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Berlin, Heidelberg :
Springer Berlin Heidelberg,
2010.
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| Series: | Studies in Computational Intelligence,
291 |
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| Online Access: | http://dx.doi.org/10.1007/978-3-642-13932-1 |
Table of Contents:
- Part 1 Introduction
- Part 2 Reinforcement Learning
- Part 3 On-Line Evolutionary Computation
- Part 4 Evolutionary Function Approximation
- Part 5 Sample-Efficient Evolutionary Function Approximation
- Part 6 Automatic Feature Selection for Reinforcement Learning
- Part 7 Adaptive Tile Coding
- Part 8 RelatedWork
- Part 9 Conclusion
- Part 10 Statistical Significance.