Optimal Transport-Assisted Risk-Sensitive Q-Learning

• URL: http://arxiv.org/abs/2406.11774v2
• Date: Wed, 11 Sep 2024 22:30:25 GMT
• Title: Optimal Transport-Assisted Risk-Sensitive Q-Learning
• Authors: Zahra Shahrooei, Ali Baheri,
• Abstract summary: This paper presents a risk-sensitive Q-learning algorithm that leverages optimal transport theory to enhance the agent safety. We validate the proposed algorithm in a Gridworld environment.
• Score: 4.14360329494344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The primary goal of reinforcement learning is to develop decision-making policies that prioritize optimal performance without considering risk or safety. In contrast, safe reinforcement learning aims to mitigate or avoid unsafe states. This paper presents a risk-sensitive Q-learning algorithm that leverages optimal transport theory to enhance the agent safety. By integrating optimal transport into the Q-learning framework, our approach seeks to optimize the policy's expected return while minimizing the Wasserstein distance between the policy's stationary distribution and a predefined risk distribution, which encapsulates safety preferences from domain experts. We validate the proposed algorithm in a Gridworld environment. The results indicate that our method significantly reduces the frequency of visits to risky states and achieves faster convergence to a stable policy compared to the traditional Q-learning algorithm.

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