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.
@article{arxiv.1704.04912,
title = {Pseudorehearsal in actor-critic agents},
author = {Marochko Vladimir and Leonard Johard and Manuel Mazzara},
journal= {arXiv preprint arXiv:1704.04912},
year = {2017}
}