Summary of Ogbench: Benchmarking Offline Goal-conditioned Rl, by Seohong Park et al.
OGBench: Benchmarking Offline Goal-Conditioned RL
by Seohong Park, Kevin Frans, Benjamin Eysenbach, Sergey Levine
First submitted to arxiv on: 26 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 A novel benchmark, OGBench, is proposed to evaluate offline goal-conditioned reinforcement learning (GCRL) algorithms. Offline GCRL allows for the acquisition of diverse behaviors and representations from unlabeled data without rewards. The existing lack of a standard benchmark hinders the evaluation of these algorithms’ capabilities. OGBench addresses this by providing a high-quality benchmark with 8 environment types, 85 datasets, and reference implementations of 6 representative offline GCRL algorithms. These environments and datasets are designed to probe different algorithmic capabilities, such as stitching, long-horizon reasoning, and handling high-dimensional inputs and stochasticity. Experimental results reveal strengths and weaknesses in these capabilities, providing a foundation for building new algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline goal-conditioned reinforcement learning (GCRL) is important because it helps computers learn from data without rewards. Right now, we don’t have a good way to test how well GCRL algorithms work. To fix this, researchers are introducing a new benchmark called OGBench. This benchmark has many different environments and datasets that help us understand what GCRL algorithms can do. It also includes examples of 6 important GCRL algorithms. By testing these algorithms on the different environments and datasets, we can see which ones are good at certain tasks. |
Keywords
» Artificial intelligence » Reinforcement learning
