Summary of Expert Proximity As Surrogate Rewards For Single Demonstration Imitation Learning, by Chia-cheng Chiang et al.
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning
by Chia-Cheng Chiang, Li-Cheng Lan, Wei-Fang Sun, Chien Feng, Cho-Jui Hsieh, Chun-Yi Lee
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a novel approach to imitation learning (IL) called Transition Discriminator-based IL (TDIL), which addresses the issue of sparse reward signals in single-demonstration IL settings. In this setting, an agent has access to only one expert trajectory and must learn from it without knowing the ground truth reward function. TDIL introduces a denser surrogate reward function that considers environmental dynamics to encourage the agent to navigate towards states that are proximal to expert states. The method trains a transition discriminator to differentiate between valid and non-valid transitions in a given environment, allowing the agent to compute the surrogate rewards. Experimental results demonstrate that TDIL outperforms existing IL approaches and achieves expert-level performance across five MuJoCo benchmarks and the “Adroit Door” robotic environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re teaching a robot how to do a task, but all you can show it is one example of someone doing it correctly. That’s basically the challenge faced by imitation learning (IL) in real-world applications. This paper presents a new approach called TDIL that helps robots learn from just one demonstration, even if they don’t know what makes the action correct. The idea is to create a better reward system that takes into account how the environment works, rather than just saying “good job” or “bad job”. The results show that this approach can help robots perform tasks as well as humans. |
