Summary of Can Learned Optimization Make Reinforcement Learning Less Difficult?, by Alexander David Goldie et al.
Can Learned Optimization Make Reinforcement Learning Less Difficult?
by Alexander David Goldie, Chris Lu, Matthew Thomas Jackson, Shimon Whiteson, Jakob Nicolaus Foerster
First submitted to arxiv on: 9 Jul 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 research paper explores the challenges faced by reinforcement learning (RL) in real-world decision making, including non-stationarity, plasticity loss, and exploration requirements. The authors propose Learned Optimization for Plasticity, Exploration, and Non-Stationarity (OPEN), a meta-learning approach that adapts to diverse learning contexts. OPEN’s input features and output structure are informed by existing solutions to these difficulties. Experimental results show that OPEN outperforms or equals traditional optimizers when trained on single and small sets of environments, demonstrating strong generalization capabilities across various agent architectures and environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a powerful tool for making decisions in the real world. However, it faces some big challenges. This research paper looks at how to overcome these problems using a new approach called Learned Optimization for Plasticity, Exploration, and Non-Stationarity (OPEN). OPEN learns from previous solutions to make better decisions. The results show that OPEN is very good at making decisions in different situations and can work well with different types of agents. |
Keywords
* Artificial intelligence * Generalization * Meta learning * Optimization * Reinforcement learning
