Summary of Efficient Imitation Learning with Conservative World Models, by Victor Kolev et al.
Efficient Imitation Learning with Conservative World Models
by Victor Kolev, Rafael Rafailov, Kyle Hatch, Jiajun Wu, Chelsea Finn
First submitted to arxiv on: 21 May 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 This paper addresses the challenge of learning policies from expert demonstrations without a reward function, a crucial problem in areas like robotics and artificial intelligence. The authors argue that traditional methods, such as adversarial imitation learning, require additional on-policy training samples for stability, which is inefficient and high in sample complexity. Instead, they propose re-framing imitation learning as a fine-tuning problem, rather than pure reinforcement learning. They derive a principled conservative optimization bound and demonstrate its effectiveness on two challenging environments from raw pixel observations, achieving state-of-the-art performance on the Franka Kitchen environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn policies from expert demonstrations without a reward function. Usually, these policies don’t work well in real-life situations because they haven’t seen all the possible scenarios. The authors found that traditional methods need more training data to be stable, which is not practical. They have a new idea: instead of learning everything from scratch, we can fine-tune what we already know. This approach works better and requires less data. They tested it on two difficult environments and achieved impressive results. |
Keywords
» Artificial intelligence » Fine tuning » Optimization » Reinforcement learning
