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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...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2009.
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| Edición: | 1st ed. 2009. |
| Colección: | Studies in Computational Intelligence,
216 |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- 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.