The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning
Abstract
We study the multi-step off-policy learning approach to distributional RL. Despite the apparent similarity between value-based RL and distributional RL, our study reveals intriguing and fundamental differences between the two cases in the multi-step setting. We identify a novel notion of path-dependent distributional TD error, which is indispensable for principled multi-step distributional RL. The distinction from the value-based case bears important implications on concepts such as backward-view algorithms. Our work provides the first theoretical guarantees on multi-step off-policy distributional RL algorithms, including results that apply to the small number of existing approaches to multi-step distributional RL. In addition, we derive a novel algorithm, Quantile Regression-Retrace, which leads to a deep RL agent QR-DQN-Retrace that shows empirical improvements over QR-DQN on the Atari-57 benchmark. Collectively, we shed light on how unique challenges in multi-step distributional RL can be addressed both in theory and practice.
Cite
@article{arxiv.2207.07570,
title = {The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning},
author = {Yunhao Tang and Mark Rowland and Rémi Munos and Bernardo Ávila Pires and Will Dabney and Marc G. Bellemare},
journal= {arXiv preprint arXiv:2207.07570},
year = {2022}
}