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ABSTRACT Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning
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@MISC{Taylor_abstracttransfer,
author = {Matthew E. Taylor and Shimon Whiteson and Peter Stone},
title = {ABSTRACT Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning},
year = {}
}
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Abstract
The ambitious goal of transfer learning is to accelerate learning on a target task after training on a different, but related, source task. While many past transfer methods have focused on transferring value-functions, this paper presents a method for transferring policies across tasks with different state and action spaces. In particular, this paper utilizes transfer via inter-task mappings for policy search methods (TVITM-PS) to construct a transfer functional that translates a population of neural network policies trained via policy search from a source task to a target task. Empirical results in robot soccer Keepaway and Server Job Scheduling show that TVITM-PS can markedly reduce learning time when full inter-task mappings are available. The results also demonstrate that TVITM-PS still succeeds when given only incomplete inter-task mappings. Furthermore, we present a novel method for learning such mappings when they are not available, and give results showing they perform comparably to hand-coded mappings. 1.
Keyphrases
inter-task mapping abstract transfer policy search reinforcement learning source task target task robot soccer keepaway many past transfer method empirical result neural network policy transfer functional hand-coded mapping server job scheduling policy search method different state action space novel method ambitious goal policy search full inter-task mapping incomplete inter-task mapping transfer learning
