Summary of Extremum-seeking Action Selection For Accelerating Policy Optimization, by Ya-chien Chang and Sicun Gao
Extremum-Seeking Action Selection for Accelerating Policy Optimization
by Ya-Chien Chang, Sicun Gao
First submitted to arxiv on: 2 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 The paper proposes a novel approach to improve reinforcement learning for continuous spaces by introducing adaptive control steps based on Extremum-Seeking Control (ESC). It addresses the issue of low-value trajectories generated from high-entropy stochastic policies and slow or failed learning. The method applies sinusoidal perturbations and queries estimated Q-values as response signals, dynamically improving action selection to be closer to nearby optima. This approach can be easily integrated into standard policy optimization and is demonstrated in various control learning environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper helps machines learn better by making small changes to the actions they take. It’s like finding the best path for a robot to follow without getting stuck or going off track. The researchers developed a new way to make these changes based on an existing method called Extremum-Seeking Control. This new approach can be used in different situations where robots need to learn how to control their movements. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
