Summary of Carel: Instruction-guided Reinforcement Learning with Cross-modal Auxiliary Objectives, by Armin Saghafian et al.
CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives
by Armin Saghafian, Amirmohammad Izadi, Negin Hashemi Dijujin, Mahdieh Soleymani Baghshah
First submitted to arxiv on: 29 Nov 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 proposes CAREL (Cross-modal Auxiliary REinforcement Learning), a novel framework for solving language-guided goal-reaching reinforcement learning problems. The framework uses auxiliary loss functions inspired by video-text retrieval literature and an instruction tracking method to enhance the model’s ability to generalize across tasks and environments. In experiments, CAREL shows superior sample efficiency and systematic generalization in multi-modal reinforcement learning problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CAREL helps robots or AI agents understand instructions better so they can complete tasks successfully. It uses new ideas from video-text matching and a way to track progress in an environment. This makes the agent smarter and more efficient at learning, especially when it has to adapt to different situations. |
Keywords
» Artificial intelligence » Generalization » Multi modal » Reinforcement learning » Tracking
