Pseudorehearsal in actor-critic agents with neural network function approximation

2017-12-20 · Vladimir Marochko, Leonard Johard, Manuel Mazzara, Luca Longo

Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested agent in a pole balancing task and compared different pseudorehearsal approaches. We have found that pseudorehearsal can assist learning and decrease forgetting.

📄 PDF Abstract BibTeX arXiv:1712.07686

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reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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 i…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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