Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation. (arXiv:2109.00525v2 [cs.LG] UPDATED)

The powerful learning ability of deep neural networks enables reinforcement
learning agents to learn competent control policies directly from continuous
environments. In theory, to achieve stable performance, neural networks assume
i.i.d. inputs, which unfortunately does no hold in the general reinforcement
learning paradigm where the training data is temporally correlated and
non-stationary. This issue may lead to the phenomenon of "catastrophic
interference" and the collapse in performance. In this paper, we present IQ,
i.e., interference-aware deep Q-learning, to mitigate catastrophic …

arxiv context distillation knowledge learning reinforcement reinforcement learning solution