  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.
