Summary of C-gail: Stabilizing Generative Adversarial Imitation Learning with Control Theory, by Tianjiao Luo et al.
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory
by Tianjiao Luo, Tim Pearce, Huayu Chen, Jianfei Chen, Jun Zhu
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 a novel approach to improve the training stability of Generative Adversarial Imitation Learning (GAIL), which trains generative policies to mimic demonstrations using on-policy Reinforcement Learning. The current instability issues with GAIL are addressed through control-theoretic analysis, leading to the development of Controlled-GAIL (C-GAIL). Experimental results show that C-GAIL outperforms vanilla GAIL and GAIL-DAC in terms of convergence rate and distribution matching. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make Generative Adversarial Imitation Learning (GAIL) better. GAIL is a way to train computers to do tasks by watching someone else do it. Right now, GAIL has some big problems that stop it from working well. The people who made this paper used ideas from control theory to fix these issues. They created a new way to train GAIL called Controlled-GAIL (C-GAIL). Tests showed that C-GAIL works better than the old ways. |
Keywords
* Artificial intelligence * Reinforcement learning
