Summary of Policy Learning For Off-dynamics Rl with Deficient Support, by Linh Le Pham Van and Hung the Tran and Sunil Gupta
Policy Learning for Off-Dynamics RL with Deficient Support
by Linh Le Pham Van, Hung The Tran, Sunil Gupta
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel approach for adapting reinforcement learning (RL) policies to real-world environments that exhibit significant dynamics discrepancies with the source simulator. The current methods require the source domain to cover all possible target transitions, which is often unrealistic. The authors shift the focus from full support to addressing large dynamics mismatch adaptation and develop an effective policy for the target domain. Their proposed method leverages skewing and extension of source support towards target support to mitigate support deficiencies. The approach is simple yet effective, as demonstrated by comprehensive testing on a varied set of benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers find a way to adapt reinforcement learning (RL) policies from computer simulations to real-world situations. They show that current methods need the simulator to cover all possible actions in the real world, which isn’t always possible. Instead, they develop a new approach that helps RL learn from simulations even when there are big differences between the simulation and the real world. Their method works by adjusting what it learns from the simulation to better match the real-world situation. |
Keywords
* Artificial intelligence * Reinforcement learning
