Summary of Growing Q-networks: Solving Continuous Control Tasks with Adaptive Control Resolution, by Tim Seyde et al.
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution
by Tim Seyde, Peter Werner, Wilko Schwarting, Markus Wulfmeier, Daniela Rus
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 bridges the performance gap between bang-bang policies and smooth control signals in robotics applications by growing discrete action spaces from coarse to fine control resolution. The approach leverages decoupled Q-learning to scale up to high-dimensional action spaces of dimension 38. By combining adaptive control resolution with value decomposition, simple critic-only algorithms are developed that achieve surprisingly strong performance on continuous control tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers use a new method to improve the performance of robots by making their movements smoother and more efficient. They do this by gradually increasing the number of possible actions the robot can take, from a small set to a larger one. This allows the robot to explore its environment better while still achieving good results. |
