Summary of Odrl: a Benchmark For Off-dynamics Reinforcement Learning, by Jiafei Lyu et al.
ODRL: A Benchmark for Off-Dynamics Reinforcement Learning
by Jiafei Lyu, Kang Xu, Jiacheng Xu, Mengbei Yan, Jingwen Yang, Zongzhang Zhang, Chenjia Bai, Zongqing Lu, Xiu Li
First submitted to arxiv on: 28 Oct 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 This paper addresses the challenge of off-dynamics reinforcement learning (RL), where policies need to be transferred across different domains with dynamics mismatches. The authors introduce ODRL, a benchmark tailored for evaluating off-dynamics RL methods, comprising four experimental settings with diverse tasks and dynamics shifts. The benchmark includes recent algorithms and extra baselines in a unified framework, implemented in a single file. Extensive benchmarking experiments show that no method has universal advantages across varied dynamics shifts. The authors hope ODRL will serve as a cornerstone for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Off-dynamics reinforcement learning is like teaching a robot to adapt to new situations. Imagine you have a robot that can play tennis well, but it’s not good at playing basketball. To make the robot better at basketball, we need to teach it how to adapt to the new game. This paper makes it easier for researchers to test and compare different ways of teaching robots (or AI agents) to adapt to new situations by creating a special benchmark called ODRL. |
Keywords
» Artificial intelligence » Reinforcement learning
