Pseudorehearsal in actor-critic agents
  Catastrophic forgetting has a serious impact in reinforcement learning, as
the data distribution is generally sparse and non-stationary over time. The
purpose of this study is to investigate whether pseudorehearsal can increase
performance of an actor-critic agent with neural-network based policy selection
and function approximation in a pole balancing task and compare different
pseudorehearsal approaches. We expect that pseudorehearsal assists learning
even in such very simple problems, given proper initialization of the rehearsal
parameters.
