Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2409.06356v1
• Date: Tue, 10 Sep 2024 09:23:03 GMT
• Title: Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning
• Authors: Shreyas S R,
• Abstract summary: Successive Over-Relaxation (SOR) Q-learning, which introduces a relaxation factor to speed up convergence, has two major limitations. We propose a sample-based, model-free double SOR Q-learning algorithm. The proposed algorithm is extended to large-scale problems using deep RL.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Q-learning is a widely used algorithm in reinforcement learning (RL), but its convergence can be slow, especially when the discount factor is close to one. Successive Over-Relaxation (SOR) Q-learning, which introduces a relaxation factor to speed up convergence, addresses this issue but has two major limitations: In the tabular setting, the relaxation parameter depends on transition probability, making it not entirely model-free, and it suffers from overestimation bias. To overcome these limitations, we propose a sample-based, model-free double SOR Q-learning algorithm. Theoretically and empirically, this algorithm is shown to be less biased than SOR Q-learning. Further, in the tabular setting, the convergence analysis under boundedness assumptions on iterates is discussed. The proposed algorithm is extended to large-scale problems using deep RL. Finally, the tabular version of the proposed algorithm is compared using roulette and grid world environments, while the deep RL version is tested on a maximization bias example and OpenAI Gym environments.

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