Summary of Ace : Off-policy Actor-critic with Causality-aware Entropy Regularization, by Tianying Ji et al.
ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization
by Tianying Ji, Yongyuan Liang, Yan Zeng, Yu Luo, Guowei Xu, Jiawei Guo, Ruijie Zheng, Furong Huang, Fuchun Sun, Huazhe Xu
First submitted to arxiv on: 22 Feb 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 introduces a new model-free RL algorithm that leverages the varying significance of primitive behaviors during policy learning. The authors propose a causality-aware entropy term to identify and prioritize actions with high potential impacts, as well as a dormancy-guided reset mechanism to prevent excessive focus on specific primitive behaviors. The resulting algorithm, ACE, outperforms model-free RL baselines across 29 continuous control tasks spanning 7 domains, demonstrating its effectiveness, versatility, and efficient sample efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores the relationship between different action dimensions and rewards to evaluate the significance of various primitive behaviors during training. It introduces a causality-aware entropy term that helps identify and prioritize actions with high potential impacts for efficient exploration. The authors also analyze the gradient dormancy phenomenon and introduce a dormancy-guided reset mechanism to prevent excessive focus on specific primitive behaviors. |
