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Open Access

Offline Reinforcement Learning with Constrained Hybrid Action Implicit Representation Towards Wargaming Decision-Making

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China; and also with Zhongguancun Laboratory, Beijing 100191, China
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; and also with State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China
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Abstract

Reinforcement Learning (RL) has emerged as a promising data-driven solution for wargaming decision-making. However, two domain challenges still exist: (1) dealing with discrete-continuous hybrid wargaming control and (2) accelerating RL deployment with rich offline data. Existing RL methods fail to handle these two issues simultaneously, thereby we propose a novel offline RL method targeting hybrid action space. A new constrained action representation technique is developed to build a bidirectional mapping between the original hybrid action space and a latent space in a semantically consistent way. This allows learning a continuous latent policy with offline RL with better exploration feasibility and scalability and reconstructing it back to a needed hybrid policy. Critically, a novel offline RL optimization objective with adaptively adjusted constraints is designed to balance the alleviation and generalization of out-of-distribution actions. Our method demonstrates superior performance and generality across different tasks, particularly in typical realistic wargaming scenarios.

Keywords

decision-makingwargaminghybrid action spaceoffline Reinforcement Learning (RL)

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Tsinghua Science and Technology
Volume 29 Issue 5,
October 2024
Pages 1422-1440
DOI: 10.26599/TST.2023.9010100
Cite this article:
Dong L, Li N, Gong G, et al. Offline Reinforcement Learning with Constrained Hybrid Action Implicit Representation Towards Wargaming Decision-Making. Tsinghua Science and Technology, 2024, 29(5): 1422-1440. https://doi.org/10.26599/TST.2023.9010100

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Received: 24 July 2023
Revised: 31 August 2023
Accepted: 18 September 2023
Published: 02 May 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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