Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large Deviations

Gillman, Edward; Rose, Dominic C.; Garrahan, Juan P.

Abstract

We present a framework to integrate tensor network (TN) methods with reinforcement learning (RL) for solving dynamical optimization tasks. We consider the RL actor-critic method, a model-free approach for solving RL problems, and introduce TNs as the approximators for its policy and value functions. Our "actor-critic with tensor networks"(ACTeN) method is especially well suited to problems with large and factorizable state and action spaces. As an illustration of the applicability of ACTeN we solve the exponentially hard task of sampling rare trajectories in two paradigmatic stochastic models, the East model of glasses and the asymmetric simple exclusion process, the latter being particularly challenging to other methods due to the absence of detailed balance. With substantial potential for further integration with the vast array of existing RL methods, the approach introduced here is promising both for applications in physics and to multi-agent RL problems more generally.

Citation

Gillman, E., Rose, D. C., & Garrahan, J. P. (2024). Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large Deviations. Physical Review Letters, 132(19), Article 197301. https://doi.org/10.1103/PhysRevLett.132.197301

• Journal Article Type: Article
• Acceptance Date: Apr 4, 2024
• Online Publication Date: May 7, 2024
• Publication Date: May 7, 2024
• Deposit Date: Apr 5, 2024
• Publicly Available Date: Apr 5, 2024
• Journal: Physical Review Letters
• Print ISSN: 0031-9007
• Electronic ISSN: 1079-7114
• Publisher: American Physical Society
• Peer Reviewed: Peer Reviewed
• Volume: 132
• Issue: 19
• Article Number: 197301
• DOI: https://doi.org/10.1103/PhysRevLett.132.197301
• Keywords: Large deviation & rare event statistics; Machine learning; Matrix product states; Tensor network methods
• Public URL: https://nottingham-repository.worktribe.com/output/33293313