Summary of Inverse Delayed Reinforcement Learning, by Simon Sinong Zhan et al.
Inverse Delayed Reinforcement Learning
by Simon Sinong Zhan, Qingyuan Wu, Zhian Ruan, Frank Yang, Philip Wang, Yixuan Wang, Ruochen Jiao, Chao Huang, Qi Zhu
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 proposes an Inverse Reinforcement Learning (IRL) framework to extract rewarding features from expert trajectories affected by delayed disturbances. The approach employs an efficient off-policy adversarial training framework to derive expert features and recover optimal policies from augmented delayed observations. Evaluations in the MuJoCo environment under diverse delay settings demonstrate the effectiveness of this method, which outperforms using direct delayed observations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to learn from experts who are affected by delays. The authors create a new way to do this that’s more efficient and effective. They test their approach in a simulated environment called MuJoCo and show that it works well even when there are different types of delays. |
Keywords
» Artificial intelligence » Reinforcement learning
