Bounded Exploration with World Model Uncertainty in Soft Actor-Critic Reinforcement Learning Algorithm
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929620473872384 |
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| author | Qiao, Ting Williams, Henry Valencia, David MacDonald, Bruce |
| author_facet | Qiao, Ting Williams, Henry Valencia, David MacDonald, Bruce |
| contents | One of the bottlenecks preventing Deep Reinforcement Learning algorithms (DRL) from real-world applications is how to explore the environment and collect informative transitions efficiently. The present paper describes bounded exploration, a novel exploration method that integrates both 'soft' and intrinsic motivation exploration. Bounded exploration notably improved the Soft Actor-Critic algorithm's performance and its model-based extension's converging speed. It achieved the highest score in 6 out of 8 experiments. Bounded exploration presents an alternative method to introduce intrinsic motivations to exploration when the original reward function has strict meanings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06139 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bounded Exploration with World Model Uncertainty in Soft Actor-Critic Reinforcement Learning Algorithm Qiao, Ting Williams, Henry Valencia, David MacDonald, Bruce Machine Learning Systems and Control One of the bottlenecks preventing Deep Reinforcement Learning algorithms (DRL) from real-world applications is how to explore the environment and collect informative transitions efficiently. The present paper describes bounded exploration, a novel exploration method that integrates both 'soft' and intrinsic motivation exploration. Bounded exploration notably improved the Soft Actor-Critic algorithm's performance and its model-based extension's converging speed. It achieved the highest score in 6 out of 8 experiments. Bounded exploration presents an alternative method to introduce intrinsic motivations to exploration when the original reward function has strict meanings. |
| title | Bounded Exploration with World Model Uncertainty in Soft Actor-Critic Reinforcement Learning Algorithm |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2412.06139 |