The deployment of reinforcement learning (RL) in the real world comes with challenges in calibrating user trust and expectations. As a step toward developing RL systems that are able to communicate their competencies, we present a method of generating human-interpretable abstract behavior models that identify the experiential conditions leading to different task execution strategies and outcomes. Our approach consists of extracting experiential features from state representations, abstracting strategy descriptors from trajectories, and training an interpretable decision tree that identifies the conditions most predictive of different RL behaviors. We demonstrate our method on trajectory data generated from interactions with the environment and on imagined trajectory data that comes from a trained probabilistic world model in a model-based RL setting.

中文翻译：

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从真实和想象数据解释强化学习行为的条件

在现实世界中部署强化学习 (RL) 带来了校准用户信任和期望方面的挑战。作为开发能够传达其能力的 RL 系统的一步，我们提出了一种生成人类可解释的抽象行为模型的方法，该模型识别导致不同任务执行策略和结果的经验条件。我们的方法包括从状态表示中提取经验特征，从轨迹中提取策略描述符，以及训练一个可解释的决策树，以识别最能预测不同 RL 行为的条件。