Transfer in Reinforcement Learning Domains
In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow...
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| Format: | eBook |
| Language: | English |
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Berlin, Heidelberg
Springer Berlin Heidelberg
2009, 2009
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| Edition: | 1st ed. 2009 |
| Series: | Studies in Computational Intelligence
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| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Reinforcement Learning Background
- Related Work
- Empirical Domains
- Value Function Transfer via Inter-Task Mappings
- Extending Transfer via Inter-Task Mappings
- Transfer between Different Reinforcement Learning Methods
- Learning Inter-Task Mappings
- Conclusion and Future Work