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- W2899403804 abstract "In this paper, a deep reinforcement learning approach was implemented to achieve autonomous collision avoidance. A transfer reinforcement learning approach (TRL) was proposed by introducing two concepts: transfer belief — how much confidence the agent puts in the expert’s experience, and transfer period — how long the agent’s decision is influenced by the expert’s experience. Various case studies have been conducted on transfer from a simple task — single static obstacle, to a complex task — multiple dynamic obstacles. It is found that if two tasks have low similarity, it is better to decrease initial transfer belief and keep a relatively longer transfer period, in order to reduce negative transfer and boost learning. Student agent’s learning variance grows significantly if using too short transfer period." @default.
- W2899403804 created "2018-11-09" @default.
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- W2899403804 date "2018-08-26" @default.
- W2899403804 modified "2023-09-23" @default.
- W2899403804 title "Design of Transfer Reinforcement Learning Under Low Task Similarity" @default.
- W2899403804 doi "https://doi.org/10.1115/detc2018-86013" @default.
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