Online Statistical Inference for Time-varying Sample-averaged Q-learning

• URL: http://arxiv.org/abs/2410.10737v1
• Date: Mon, 14 Oct 2024 17:17:19 GMT
• Title: Online Statistical Inference for Time-varying Sample-averaged Q-learning
• Authors: Saunak Kumar Panda, Ruiqi Liu, Yisha Xiang,
• Abstract summary: This paper introduces a time-varying batch-averaged Q-learning, termed sampleaveraged Q-learning. We develop a novel framework that provides insights into the normality of the sample-averaged algorithm under mild conditions. Numerical experiments conducted on classic OpenAI Gym environments show that the time-varying sample-averaged Q-learning method consistently outperforms both single-sample and constant-batch Q-learning.
• Score: 2.2374171443798034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL) has emerged as a key approach for training agents in complex and uncertain environments. Incorporating statistical inference in RL algorithms is essential for understanding and managing uncertainty in model performance. This paper introduces a time-varying batch-averaged Q-learning algorithm, termed sampleaveraged Q-learning, which improves upon traditional single-sample Q-learning by aggregating samples of rewards and next states to better account for data variability and uncertainty. We leverage the functional central limit theorem (FCLT) to establish a novel framework that provides insights into the asymptotic normality of the sample-averaged algorithm under mild conditions. Additionally, we develop a random scaling method for interval estimation, enabling the construction of confidence intervals without requiring extra hyperparameters. Numerical experiments conducted on classic OpenAI Gym environments show that the time-varying sample-averaged Q-learning method consistently outperforms both single-sample and constant-batch Q-learning methods, achieving superior accuracy while maintaining comparable learning speeds.

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