Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs

Philips George John; Arnab Bhattacharyya; Silviu Maniu; Dimitrios Myrisiotis; Zhenan Wu

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arXiv · 2411.10906

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs

Philips George John·Arnab Bhattacharyya·Silviu Maniu·Dimitrios Myrisiotis·Zhenan Wu

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Abstract

Reinforcement learning algorithms are usually stated without theoretical guarantees regarding their performance. Recently, Jin, Yang, Wang, and Jordan (COLT 2020) showed a polynomial-time reinforcement learning algorithm (namely, LSVI-UCB) for the setting of linear Markov decision processes, and provided theoretical guarantees regarding its running time and regret. In real-world scenarios, however, the space usage of this algorithm can be prohibitive due to a utilized linear regression step. We propose and analyze two modifications of LSVI-UCB, which alternate periods of learning and not-learning, to reduce space and time usage while maintaining sublinear regret. We show experimentally, on synthetic data and real-world benchmarks, that our algorithms achieve low space usage and running time, while not significantly sacrificing regret.

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Philips George John, Arnab Bhattacharyya, Silviu Maniu, Dimitrios Myrisiotis, Zhenan Wu. 2024-11-16. Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs. https://arxiv.org/abs/2411.10906

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