Summary of A Study Of Plasticity Loss in On-policy Deep Reinforcement Learning, by Arthur Juliani et al.
A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
by Arthur Juliani, Jordan T. Ash
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the challenges of continual learning with deep neural networks, focusing on the problem of plasticity loss in online learning. In supervised learning and off-policy reinforcement learning (RL), remedies have been proposed to mitigate this issue. However, in the on-policy deep RL setting, plasticity loss has received less attention. The authors conduct extensive experiments to examine plasticity loss and various mitigation methods in on-policy deep RL. They find that many methods developed for other settings fail or even worsen the problem. In contrast, a class of “regenerative” methods consistently mitigate plasticity loss across various contexts, including gridworld tasks and challenging environments like Montezuma’s Revenge and ProcGen. The study contributes to our understanding of on-policy deep RL and provides insights for improving performance in this regime. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how artificial intelligence (AI) networks learn new things while also remembering what they learned before. This is important because it can help AI systems get better over time, but it’s tricky to make them do both well. The researchers tested different ways to solve this problem and found that some methods didn’t work as well as expected. However, they discovered a few approaches that consistently helped the AI networks learn new things without forgetting what they knew before. This is an important step forward in making AI systems smarter and more useful. |
Keywords
» Artificial intelligence » Attention » Continual learning » Online learning » Reinforcement learning » Supervised
