Summary of Adversarial Environment Design Via Regret-guided Diffusion Models, by Hojun Chung et al.
Adversarial Environment Design via Regret-Guided Diffusion Models
by Hojun Chung, Junseo Lee, Minsoo Kim, Dohyeong Kim, Songhwai Oh
First submitted to arxiv on: 25 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 In this paper, researchers tackle the challenge of training agents that are robust to environmental changes in deep reinforcement learning. They propose a novel algorithm called adversarial environment design via regret-guided diffusion models (ADD) to generate training environments tailored to an agent’s capabilities. ADD guides the environment generator with the regret of the agent to produce challenging yet conducive environments, allowing the agent to learn a robust policy. The proposed method exploits the representation power of diffusion models to directly generate adversarial environments while maintaining diversity in training environments. Experimental results demonstrate that ADD outperforms UED baselines in zero-shot generalization across novel, out-of-distribution environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us train better agents that can adapt to changing environments. The researchers create a new way to make the environment more challenging and helpful for learning by using something called regret-guided diffusion models. This makes the agent learn faster and do better in situations it hasn’t seen before. They show that their method is better than others at doing this. |
Keywords
» Artificial intelligence » Diffusion » Generalization » Reinforcement learning » Zero shot
