Summary of Overcoming the Sim-to-real Gap: Leveraging Simulation to Learn to Explore For Real-world Rl, by Andrew Wagenmaker et al.
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL
by Andrew Wagenmaker, Kevin Huang, Liyiming Ke, Byron Boots, Kevin Jamieson, Abhishek Gupta
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); Machine Learning (stat.ML)
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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 proposed approach in this paper targets the challenge of sample complexity in reinforcement learning by leveraging simulation environments. The common strategy is to first train a policy in a simulator and then deploy it in the real world, assuming generalization will occur. However, direct sim2real transfer may not always succeed, leaving uncertainty on how to utilize the simulator effectively. This work shows that even when direct sim2real transfer fails, the simulator can still be used to learn exploratory policies for efficient exploration in the real world. In low-rank MDP settings, combining these exploratory policies with simple approaches like least-squares regression oracles and randomized exploration yields a polynomial sample complexity in the real world, outperforming direct sim2real transfer. Theoretical results are validated on realistic robotic simulators and a real-world robotic task, demonstrating practical gains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how to make robots learn better by using simulations first. Right now, people usually train a robot in a fake environment called a simulator, then hope it will work well in the real world. But sometimes this doesn’t work, and they’re not sure what to do next. This research shows that even when training in a simulator doesn’t work directly, you can still use the simulator to teach the robot how to explore its surroundings more efficiently. The idea is that by using simple methods, like using past experiences or making random choices, the robot can learn quickly and effectively in the real world. The results are promising and show that this method could be useful for real-world applications. |
Keywords
» Artificial intelligence » Generalization » Regression » Reinforcement learning
