A Distributional Analogue to the Successor Representation
<|reference_start|>A Distributional Analogue to the Successor Representation: 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.<|reference_end|>
@article{wiltzer2024a,
    title={A Distributional Analogue to the Successor Representation},
    author={Harley Wiltzer and Jesse Farebrother and Arthur Gretton and Yunhao
  Tang and Andr'e Barreto and Will Dabney and Marc G. Bellemare and Mark
  Rowland},
    journal={arXiv preprint arXiv:2402.08530},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08530},
    primaryClass={cs.LG cs.AI stat.ML}
}