Bridging Exploration and Loss of Plasticity in On-Policy Reinforcement Learning

arXiv:2610.36309v1 Announce Type: new Abstract: Deep reinforcement learning (RL) algorithms have been observed to lose the ability to adaptively learn from new data, a phenomenon termed Loss of Plasticity (LoP). While many metrics have been shown to correlate with it, the causal mechanisms and the geometric conditions from which it can arise have not been clearly identified. We analyze LoP in Proximal Policy Optimization with isotropic Gaussian policies, the state-of-the-art formulation for many continuous action spaces in RL. We report multiple metrics which characterize the effect that the policy standard deviation $\sigma$ has on LoP, and we also suggest related explanations for the effectiveness of widely adopted LoP mitigation techniques.