Towards Understanding the Impact of Plasticity Loss on Reinforcement Learning in Stochastic Environments
Read the original on arXiv Machine Learning →The paper investigates how plasticity loss affects deep reinforcement learning in stochastic environments. By training SAC agents on DeepMind Control tasks with varying observation and action noise, the authors find that plasticity-preserving techniques improve robustness, especially as noise increases, while resets combined with layer normalization perform worse. They also observe that stochasticity can accelerate plasticity loss in harder tasks, but in easier settings noise can act as a regularizer, and that plasticity loss is most pronounced early in training, with TD instability dominating in the most challenging scenarios.
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