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

• URL: http://arxiv.org/abs/2412.06139v1
• Date: Mon, 09 Dec 2024 01:45:08 GMT
• Title: Bounded Exploration with World Model Uncertainty in Soft Actor-Critic Reinforcement Learning Algorithm
• Authors: Ting Qiao, Henry Williams, David Valencia, Bruce MacDonald,
• Abstract summary: Bounded exploration is a novel exploration method that integrates both'soft' and intrinsic motivation exploration. It notably improved the Soft Actor-Critic algorithm's performance and its model-based extension's converging speed.
• Score: 0.6749750044497732
• License:
• 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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