Summary of Reset & Distill: a Recipe For Overcoming Negative Transfer in Continual Reinforcement Learning, by Hongjoon Ahn et al.
Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual Reinforcement Learning
by Hongjoon Ahn, Jinu Hyeon, Youngmin Oh, Bosun Hwang, Taesup Moon
First submitted to arxiv on: 8 Mar 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 In this paper, researchers investigate the negative transfer problem that arises when Continual Reinforcement Learning (CRL) agents are asked to learn new tasks. They find that recent approaches to mitigating plasticity loss do not effectively address this issue and propose a novel method called Reset & Distill (R&D). R&D combines resetting the agent’s online actor and critic networks with offline learning for distilling knowledge from previous expert actions. The authors demonstrate the effectiveness of R&D through extensive experiments on Meta World tasks, achieving higher success rates than recent baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machines learn new things without forgetting old skills. When a machine learns something new, it can sometimes forget what it learned before. Researchers want to find ways to stop this from happening. They tried some ideas that didn’t work and then came up with a new plan called Reset & Distill. This plan helps the machine remember what it learned before while also learning new things. The researchers tested their plan on many different tasks and found that it works really well. |
Keywords
* Artificial intelligence * Reinforcement learning
