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arXiv · 2410.21151

BraVE: Offline Reinforcement Learning for Discrete Combinatorial Action Spaces

Matthew Landers·Taylor W. Killian·Hugo Barnes·Thomas Hartvigsen·Afsaneh Doryab
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Abstract

Offline reinforcement learning in high-dimensional, discrete action spaces is challenging due to the exponential scaling of the joint action space with the number of sub-actions and the complexity of modeling sub-action dependencies. Existing methods either exhaustively evaluate the action space, making them computationally infeasible, or factorize Q-values, failing to represent joint sub-action effects. We propose Branch Value Estimation (BraVE), a value-based method that uses tree-structured action traversal to evaluate a linear number of joint actions while preserving dependency structure. BraVE outperforms prior offline RL methods by up to $20\times$ in environments with over four million actions.

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Matthew Landers, Taylor W. Killian, Hugo Barnes, Thomas Hartvigsen, Afsaneh Doryab. 2024-10-28. BraVE: Offline Reinforcement Learning for Discrete Combinatorial Action Spaces. https://arxiv.org/abs/2410.21151

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