Biasing Exploration towards Positive Error for Efficient Reinforcement Learning
Efficient exploration remains a critical challenge in Reinforcement Learning (RL), significantly affecting sample efficiency. This paper demonstrates that biasing exploration towards state-action pairs with positive temporal difference error speeds up convergence and, in some challenging environmen...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2025-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Subjects: | |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/138835 |
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