Summary of Generalizing Consistency Policy to Visual Rl with Prioritized Proximal Experience Regularization, by Haoran Li et al.
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization
by Haoran Li, Zhennan Jiang, Yuhui Chen, Dongbin Zhao
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to visual reinforcement learning (RL) tackles the challenges of high-dimensional state spaces by introducing a consistency policy, dubbed CP3ER. This paper investigates how non-stationary distributions and actor-critic frameworks impact consistency policies in online RL, finding that they can be unstable, especially in visual RL with large state spaces. To address this, the authors propose sample-based entropy regularization to stabilize policy training and prioritize proximal experience regularization (CP3ER) to improve sample efficiency. CP3ER achieves state-of-the-art performance on 21 tasks across DeepMind control suite and Meta-world, demonstrating the potential of consistency models in visual RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual reinforcement learning (RL) is a challenging problem due to high-dimensional state spaces. Researchers have explored consistency models for online RL, but it’s unclear if they can be applied to visual RL. This study investigates how non-stationary distributions and actor-critic frameworks affect consistency policies in online RL. The results show that consistency policies can be unstable, especially in visual RL with large state spaces. To solve this problem, the authors suggest a new method called CP3ER, which combines sample-based entropy regularization to stabilize policy training and prioritized proximal experience regularization to improve sample efficiency. This approach achieves better results than existing methods. |
Keywords
* Artificial intelligence * Regularization * Reinforcement learning
