Summary of Adamemento: Adaptive Memory-assisted Policy Optimization For Reinforcement Learning, by Renye Yan et al.
AdaMemento: Adaptive Memory-Assisted Policy Optimization for Reinforcement Learning
by Renye Yan, Yaozhong Gan, You Wu, Junliang Xing, Ling Liangn, Yeshang Zhu, Yimao Cai
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes AdaMemento, an adaptive reinforcement learning (RL) framework that leverages memory to optimize policies. Unlike existing methods, which only store high-value policies, AdaMemento designs a memory-reflection module that learns from both positive and negative experiences by predicting local optimal policies based on real-time states. Additionally, the framework introduces a fine-grained intrinsic motivation paradigm that distinguishes nuances in similar states for exploration guidance. The paper also theoretically proves the superiority of its new mechanisms. Through 59 experiments, AdaMemento demonstrates significant improvement over previous methods by effectively exploiting past experiences and exploring new policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research improves artificial intelligence (AI) to learn from experience. It creates a new way for AI to remember what worked well in the past and use that information to make better decisions. Unlike current methods, this approach learns from both good and bad experiences to improve decision-making. The researchers also developed a system to help the AI explore new ideas and avoid getting stuck in one routine. Through many tests, they found that this new approach works better than previous ones and can be used in various applications. |
Keywords
* Artificial intelligence * Reinforcement learning
