Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in
  RL
Miguel Suau Matthijs T. J. Spaan Frans A. Oliehoek
Reinforcement learning agents may sometimes develop habits that are effective
only when specific policies are followed. After an initial exploration phase in
which agents try out different actions, they eventually converge toward a
particular policy. When this occurs, the distribution of state-action
trajectories becomes narrower, and agents start experiencing the same
transitions again and again. At this point, spurious correlations may arise.
Agents may then pick up on these correlations and learn state representations
that do not generalize beyond the agent's trajectory distribution. In this
paper, we provide a mathematical characterization of this phenomenon, which we
refer to as policy confounding, and show, through a series of examples, when
and how it occurs in practice.