Computer Science > Machine Learning
[Submitted on 29 May 2024 (v1), last revised 1 Nov 2024 (this version, v2)]
Title:A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
View PDF HTML (experimental)Abstract:Continual learning with deep neural networks presents challenges distinct from both the fixed-dataset and convex continual learning regimes. One such challenge is plasticity loss, wherein a neural network trained in an online fashion displays a degraded ability to fit new tasks. This problem has been extensively studied in both supervised learning and off-policy reinforcement learning (RL), where a number of remedies have been proposed. Still, plasticity loss has received less attention in the on-policy deep RL setting. Here we perform an extensive set of experiments examining plasticity loss and a variety of mitigation methods in on-policy deep RL. We demonstrate that plasticity loss is pervasive under domain shift in this regime, and that a number of methods developed to resolve it in other settings fail, sometimes even performing worse than applying no intervention at all. In contrast, we find that a class of ``regenerative'' methods are able to consistently mitigate plasticity loss in a variety of contexts, including in gridworld tasks and more challenging environments like Montezuma's Revenge and ProcGen.
Submission history
From: Arthur Juliani [view email][v1] Wed, 29 May 2024 14:59:49 UTC (21,470 KB)
[v2] Fri, 1 Nov 2024 16:47:59 UTC (21,515 KB)
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