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arXiv · 2205.07802

The Primacy Bias in Deep Reinforcement Learning

Evgenii Nikishin·Max Schwarzer·Pierluca D'Oro·Pierre-Luc Bacon·Aaron Courville
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Abstract

This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. Because of training on progressively growing datasets, deep RL agents incur a risk of overfitting to earlier experiences, negatively affecting the rest of the learning process. Inspired by cognitive science, we refer to this effect as the primacy bias. Through a series of experiments, we dissect the algorithmic aspects of deep RL that exacerbate this bias. We then propose a simple yet generally-applicable mechanism that tackles the primacy bias by periodically resetting a part of the agent. We apply this mechanism to algorithms in both discrete (Atari 100k) and continuous action (DeepMind Control Suite) domains, consistently improving their performance.

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BibTeXRIS

Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron Courville. 2022-05-16. The Primacy Bias in Deep Reinforcement Learning. https://arxiv.org/abs/2205.07802

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