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arXiv · 2207.07570

The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning

Yunhao Tang·Mark Rowland·Rémi Munos·Bernardo Ávila Pires·Will Dabney·Marc G. Bellemare
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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.

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Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare. 2022-07-15. The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. https://arxiv.org/abs/2207.07570

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