Pseudorehearsal in actor-critic agents

2017-04-17 · Marochko Vladimir, Leonard Johard, Manuel Mazzara

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.

📄 PDF Abstract BibTeX arXiv:1704.04912

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reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Pseudorehearsal in actor-critic agents with neural network function approximation

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