Summary of Llm-based Offline Learning For Embodied Agents Via Consistency-guided Reward Ensemble, by Yujeong Lee et al.
LLM-Based Offline Learning for Embodied Agents via Consistency-Guided Reward Ensemble
by Yujeong Lee, Sangwoo Shin, Wei-Jin Park, Honguk Woo
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The research paper explores a novel approach to enable embodied agents by leveraging large language models (LLMs) as tools for learning. Instead of directly using LLMs as agents, the authors employ them to provide dense reward feedback on individual actions in training datasets. This enables the offline reinforcement learning (RL) of separate agents via an adaptive ensemble of spatio-temporally consistent rewards. The framework, called CoREN, is designed to tackle difficulties in grounding LLM-generated estimates to the target environment domain. Experiments with the VirtualHome benchmark demonstrate that CoREN significantly outperforms other offline RL agents and achieves comparable performance to state-of-the-art LLM-based agents despite having fewer parameters. This work showcases the potential of using LLMs as tools for embodied agent learning, which can lead to more effective offline learning in different environment domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Embodied agents are like robots that learn how to perform tasks by practicing and receiving feedback. The authors of this paper found a way to use powerful language models (LLMs) to help these agents learn faster and better. Instead of using the LLMs directly, they used them to give helpful hints on what actions to take in different situations. This allowed the agents to learn more efficiently offline, which means they can practice without actually interacting with the environment. The new approach, called CoREN, was tested with a virtual home environment and showed better results than other methods. This research is important because it can help create more intelligent robots that can adapt to new situations. |
Keywords
» Artificial intelligence » Grounding » Reinforcement learning
