Summary of Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-optimal Algorithm, by Miao Lu et al.
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm
by Miao Lu, Han Zhong, Tong Zhang, Jose Blanchet
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper tackles the sim-to-real gap in reinforcement learning (RL) by proposing a new approach called distributionally robust RL, which aims to find a robust policy that achieves good performance under the worst-case scenario. Unlike previous work, this framework uses interactive data collection, where the learner interacts with the training environment and refines the policy through trial and error. The main challenges are managing distributional robustness while balancing exploration and exploitation during data collection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make artificial intelligence (AI) learn better by making sure it can work well in different situations, even if they’re very different from what it was trained on. This is called the “sim-to-real” problem because AI is often trained using simulations that are not exactly like real life. The authors propose a new way of solving this problem by having the AI learn through trial and error, rather than relying on big datasets or fancy models. They also prove that some assumptions are needed to make sure their method works well. |
Keywords
* Artificial intelligence * Reinforcement learning
