Summary of Plasticity Loss in Deep Reinforcement Learning: a Survey, by Timo Klein et al.
Plasticity Loss in Deep Reinforcement Learning: A Survey
by Timo Klein, Lukas Miklautz, Kevin Sidak, Claudia Plant, Sebastian Tschiatschek
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 A novel survey on plasticity loss in deep reinforcement learning (RL) agents is proposed. Deep RL models can adapt quickly to new data due to their plasticity, which enables them to improve their policy. However, this plasticity can be lost, leading to performance plateauing and a range of issues, including training instabilities, scaling failures, overestimation bias, and insufficient exploration. To address these challenges, the authors provide a unified definition of plasticity loss, relate it to existing definitions, and discuss metrics for measuring plasticity loss. They also categorize and discuss various causes of plasticity loss, review mitigation strategies, and propose recommendations for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning agents can quickly adapt to new data because they are “plastic”, like the human brain. But when this plasticity is lost, agents stop improving and might even get worse. This problem affects many areas of deep reinforcement learning, including training stability, scaling up, and finding good solutions. The authors of this paper want to help researchers understand and fix these issues by proposing a new definition of plasticity loss and discussing different causes and ways to prevent it. |
Keywords
* Artificial intelligence * Deep learning * Reinforcement learning
