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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Multi-Task Reinforcement Learning: Shaping and Feature Selection
verfasst von : Matthijs Snel, Shimon Whiteson
Erschienen in: Recent Advances in Reinforcement Learning
Verlag: Springer Berlin Heidelberg
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Shaping functions can be used in multi-task reinforcement learning (RL) to incorporate knowledge from previously experienced source tasks to speed up learning on a new target task. Earlier work has not clearly motivated choices for the shaping function. This paper discusses and empirically compares several alternatives, and demonstrates that the most intuive one may not always be the best option. In addition, we extend previous work on identifying good representations for the value and shaping functions, and show that selecting the right representation results in improved generalization over tasks.