Summary of Meta-gradient Search Control: a Method For Improving the Efficiency Of Dyna-style Planning, by Bradley Burega et al.
Meta-Gradient Search Control: A Method for Improving the Efficiency of Dyna-style Planning
by Bradley Burega, John D. Martin, Luke Kapeluck, Michael Bowling
First submitted to arxiv on: 27 Jun 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 investigates how Reinforcement Learning (RL) systems can efficiently learn from imperfect environment models while being resource-constrained and operating in continual settings where dynamics change. To address this challenge, the authors propose an online meta-gradient algorithm that adjusts a probability for querying states during Dyna-style planning. The study compares the performance of this approach to conventional sampling strategies, showing improved efficiency in the planning process and overall learning process. The findings highlight the avoidance of pathologies such as inaccurate transition sampling and credit assignment stalling. This work could inform the design of large-scale model-based RL systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machines can learn from imperfect models while being resource-limited and changing environments. They come up with a new algorithm that adjusts the way it chooses which states to explore during planning. The results show this approach is better than usual methods, making it faster and more efficient. The findings also highlight some problems with usual approaches, like choosing bad transitions or getting stuck. This research could help build bigger models for machines to learn from. |
Keywords
* Artificial intelligence * Probability * Reinforcement learning
