BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions

• URL: http://arxiv.org/abs/2401.07263v1
• Date: Sun, 14 Jan 2024 11:45:05 GMT
• Title: BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions
• Authors: Xiao Liu, Jie Zhao, Wubing Chen, Mao Tan, Yongxing Su
• Abstract summary: We propose a novel self-interpretable structure, named Backbone Extract Tree (BET), to better explain the agent's behavior. At a high level, BET hypothesizes that states in which the agent consistently executes uniform decisions exhibit a reduced propensity for errors. We show BET's superiority over existing self-interpretable models in terms of explanation fidelity.
• Score: 7.139669387895207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the impressive capabilities of Deep Reinforcement Learning (DRL) agents in many challenging scenarios, their black-box decision-making process significantly limits their deployment in safety-sensitive domains. Several previous self-interpretable works focus on revealing the critical states of the agent's decision. However, they cannot pinpoint the error-prone states. To address this issue, we propose a novel self-interpretable structure, named Backbone Extract Tree (BET), to better explain the agent's behavior by identify the error-prone states. At a high level, BET hypothesizes that states in which the agent consistently executes uniform decisions exhibit a reduced propensity for errors. To effectively model this phenomenon, BET expresses these states within neighborhoods, each defined by a curated set of representative states. Therefore, states positioned at a greater distance from these representative benchmarks are more prone to error. We evaluate BET in various popular RL environments and show its superiority over existing self-interpretable models in terms of explanation fidelity. Furthermore, we demonstrate a use case for providing explanations for the agents in StarCraft II, a sophisticated multi-agent cooperative game. To the best of our knowledge, we are the first to explain such a complex scenarios using a fully transparent structure.

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