Suppressing Overestimation in Q-Learning through Adversarial Behaviors

• URL: http://arxiv.org/abs/2310.06286v3
• Date: Sat, 28 Sep 2024 07:47:15 GMT
• Title: Suppressing Overestimation in Q-Learning through Adversarial Behaviors
• Authors: HyeAnn Lee, Donghwan Lee,
• Abstract summary: This paper proposes a new Q-learning algorithm with a dummy adversarial player, which is called dummy adversarial Q-learning (DAQ) The proposed DAQ unifies several Q-learning variations to control overestimation biases, such as maxmin Q-learning and minmax Q-learning. A finite-time convergence of DAQ is analyzed from an integrated perspective by adapting an adversarial Q-learning.
• Score: 4.36117236405564
• License:
• Abstract: The goal of this paper is to propose a new Q-learning algorithm with a dummy adversarial player, which is called dummy adversarial Q-learning (DAQ), that can effectively regulate the overestimation bias in standard Q-learning. With the dummy player, the learning can be formulated as a two-player zero-sum game. The proposed DAQ unifies several Q-learning variations to control overestimation biases, such as maxmin Q-learning and minmax Q-learning (proposed in this paper) in a single framework. The proposed DAQ is a simple but effective way to suppress the overestimation bias thourgh dummy adversarial behaviors and can be easily applied to off-the-shelf reinforcement learning algorithms to improve the performances. A finite-time convergence of DAQ is analyzed from an integrated perspective by adapting an adversarial Q-learning. The performance of the suggested DAQ is empirically demonstrated under various benchmark environments.

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