Article

Study Reveals Effect of Policy Standard Deviation on Loss of Plasticity in Online Reinforcement Learning

The paper analyzes loss of plasticity (LoP) in isotropic Gaussian policies within Proximal Policy Optimization (PPO), noting that the policy standard deviation σ significantly affects the extent of LoP, with theoretical and experimental support. The experiments include LoP benchmark comparisons and real-world policy deployment on a 7-DoF robotic arm; the authors also use these findings to explain why common LoP mitigation methods are effective.

Part of the event: Research analyzes PPO policy standard deviation and loss of plasticity

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