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Finite-Time Error Analysis of Online Model-Based Q-Learning with a Relaxed Sampling Model
Reinforcement learning has witnessed significant advancements, particularly with the emergence of model-based approaches.
Suppressing Overestimation in Q-Learning through Adversarial Behaviors
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.
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