  Catastrophic forgetting is of special importance in reinforcement learning,
as the data distribution is generally non-stationary over time. We study and
compare several pseudorehearsal approaches for Q-learning with function
approximation in a pole balancing task. We have found that pseudorehearsal
seems to assist learning even in such very simple problems, given proper
initialization of the rehearsal parameters.
