This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process. Analogous to how the successor representation (SR) describes the expected consequences of behaving according to a given policy, our distributional successor measure (SM) describes the distributional consequences of this behaviour. We formulate the distributional SM as a distribution over distributions and provide theory connecting it with distributional and model-based reinforcement learning. Moreover, we propose an algorithm that learns the distributional SM from data by minimizing a two-level maximum mean discrepancy. Key to our method are a number of algorithmic techniques that are independently valuable for learning generative models of state. As an illustration of the usefulness of the distributional SM, we show that it enables zero-shot risk-sensitive policy evaluation in a way that was not previously possible.

翻译：本文贡献了一种用于分布强化学习的新方法，该方法在学习过程中清晰地分离了转移结构与奖励。类似于后继表示（SR）描述根据给定策略行动时的期望后果，我们的分布后继测度（SM）描述了这种行为下的分布后果。我们将分布SM定义为一种“分布上的分布”，并提供了将其与基于分布的强化学习和基于模型的强化学习相联系的理论。此外，我们提出了一种算法，通过最小化两层最大均值差异从数据中学习分布SM。我们方法的核心是若干独立可用于生成模型学习状态的技术。为说明分布SM的实用性，我们展示了它能够以先前无法实现的方式支持零样本风险敏感策略评估。