The Role of Exploration for Task Transfer in Reinforcement Learning

• URL: http://arxiv.org/abs/2210.06168v1
• Date: Tue, 11 Oct 2022 01:23:21 GMT
• Title: The Role of Exploration for Task Transfer in Reinforcement Learning
• Authors: Jonathan C Balloch and Julia Kim and and Jessica L Inman and Mark O Riedl
• Abstract summary: We re-examine the exploration--exploitation trade-off in the context of transfer learning. In this work, we review reinforcement learning exploration methods, define a taxonomy with which to organize them, analyze these methods' differences in the context of task transfer, and suggest avenues for future investigation.
• Score: 8.817381809671804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The exploration--exploitation trade-off in reinforcement learning (RL) is a well-known and much-studied problem that balances greedy action selection with novel experience, and the study of exploration methods is usually only considered in the context of learning the optimal policy for a single learning task. However, in the context of online task transfer, where there is a change to the task during online operation, we hypothesize that exploration strategies that anticipate the need to adapt to future tasks can have a pronounced impact on the efficiency of transfer. As such, we re-examine the exploration--exploitation trade-off in the context of transfer learning. In this work, we review reinforcement learning exploration methods, define a taxonomy with which to organize them, analyze these methods' differences in the context of task transfer, and suggest avenues for future investigation.

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