Summary of Reset-free Reinforcement Learning with World Models, by Zhao Yang et al.
Reset-free Reinforcement Learning with World Models
by Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat, Edward S. Hu
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 medium-difficulty summary of the abstract follows. In this paper, researchers investigate the application of model-based reinforcement learning (MBRL) in the reset-free setting, where agents must learn from their own experiences without human intervention. The authors demonstrate that a straightforward adaptation of MBRL can outperform existing methods while requiring less supervision. They identify limitations with this approach and propose the MoReFree agent, which prioritizes task-relevant states to enhance performance. The MoReFree agent exhibits superior data-efficiency in various reset-free tasks without environmental reward or demonstrations, significantly outperforming privileged baselines that require supervision. This work suggests model-based methods hold promise for reducing human effort in RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper explores how machines can learn from themselves without human help. The researchers show that using a specific approach called model-based reinforcement learning (MBRL) is effective in this setting, even outperforming existing methods with less supervision needed. They also propose an improved agent that works well on various tasks and doesn’t require external rewards or guidance. |
Keywords
* Artificial intelligence * Reinforcement learning
