Bounded Exploration with World Model Uncertainty in Soft Actor-Critic Reinforcement Learning Algorithm

Ting Qiao; Henry Williams; David Valencia; Bruce MacDonald

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arXiv · 2412.06139

Bounded Exploration with World Model Uncertainty in Soft Actor-Critic Reinforcement Learning Algorithm

Ting Qiao·Henry Williams·David Valencia·Bruce MacDonald

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Abstract

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.

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BibTeX RIS

Ting Qiao, Henry Williams, David Valencia, Bruce MacDonald. 2024-12-09. Bounded Exploration with World Model Uncertainty in Soft Actor-Critic Reinforcement Learning Algorithm. https://arxiv.org/abs/2412.06139

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