Summary of Inferring Behavior-specific Context Improves Zero-shot Generalization in Reinforcement Learning, by Tidiane Camaret Ndir et al.
Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning
by Tidiane Camaret Ndir, André Biedenkapp, Noor Awad
First submitted to arxiv on: 15 Apr 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 research paper, we tackle the challenge of zero-shot generalization in Reinforcement Learning (RL), where agents must adapt to entirely novel environments without additional training. To achieve robust generalization, we propose integrating the learning of context representations directly with policy learning. Our algorithm demonstrates improved generalization on various simulated domains, outperforming prior context-learning techniques in zero-shot settings. By jointly learning policy and context, our method acquires behavior-specific context representations, enabling adaptation to unseen environments and marks progress towards reinforcement learning systems that generalize across diverse real-world tasks. We utilize contextual cues, such as gravity levels, to improve generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper solves a big problem in artificial intelligence called zero-shot generalization. It’s like when you learn how to do something and then can apply it to new situations without needing more practice. The challenge is that this only works for very similar situations, but what if the situation is completely different? To solve this, the researchers developed a new way of learning that combines understanding the context with learning the rules for doing things. This helps agents adapt to entirely new environments and makes it possible for artificial intelligence systems to generalize across many real-world tasks. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning » Zero shot
