The Primacy Bias in Deep Reinforcement Learning

16 May 2022  ·  Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron Courville ·

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Atari Games 100k Atari 100k SPR + resets Mean Human-Normalized Score 0.911 # 2
Medium Human-Normalized Score 0.512 # 2


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