Reinforcement Learning Journal, vol. 4, 2024, pp. 1711–1732.
Presented at the Reinforcement Learning Conference (RLC), Amherst Massachusetts, August 9–12, 2024.
Reinforcement learning agents tend to develop habits that are effective only under specific policies. Following an initial exploration phase where agents try out different actions, they eventually converge onto a particular policy. As this occurs, the distribution over state-action trajectories becomes narrower, leading agents to repeatedly experience the same transitions. This repetitive exposure fosters spurious correlations between certain observations and rewards. Agents may then pick up on these correlations and develop simplistic habits tailored to the specific set of trajectories dictated by their policy. The problem is that these habits may yield incorrect outcomes when agents are forced to deviate from their typical trajectories, prompted by changes in the environment. This paper presents a mathematical characterization of this phenomenon, termed policy confounding, and illustrates, through a series of examples, the circumstances under which it occurs.
Miguel Suau, Matthijs T. J Spaan, and Frans A Oliehoek. "Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RL." Reinforcement Learning Journal, vol. 4, 2024, pp. 1711–1732.
BibTeX:@article{suau2024habits,
title={Bad Habits: {P}olicy Confounding and Out-of-Trajectory Generalization in {RL}},
author={Suau, Miguel and Spaan, Matthijs T. J. and Oliehoek, Frans A},
journal={Reinforcement Learning Journal},
volume={4},
pages={1711--1732},
year={2024}
}