arXiv:2411.17135v1 Announce Type: new Abstract: Employing large language models (LLMs) to enable embodied agents has become popular, yet it presents several limitations in practice. In this work, rather than using LLMs directly as agents, we explore their use as tools for embodied agent learning. Specifically, to train separate agents via offline reinforcement learning (RL), an LLM is used to provide dense reward feedback on individual actions in training datasets. In doing so, we present a consistency-guided reward ensemble framework (CoREN), designed for tackling difficulties in grounding LLM-generated estimates to the target environment domain. The framework employs an adaptive ensemble of spatio-temporally consistent rewards to derive domain-grounded rewards in the training datasets, thus enabling effective offline learning of embodied agents in different environment domains. Experiments with the VirtualHome benchmark demonstrate that CoREN significantly outperforms other offline RL agents, and it also achieves comparable performance to state-of-the-art LLM-based agents with 8B parameters, despite CoREN having only 117M parameters for the agent policy network and using LLMs only for training.
The article “Employing Large Language Models as Tools for Embodied Agent Learning” explores the limitations of using large language models (LLMs) directly as agents and proposes a new approach that leverages LLMs as tools for training embodied agents. The authors introduce the consistency-guided reward ensemble framework (CoREN), which utilizes an adaptive ensemble of spatio-temporally consistent rewards derived from LLM-generated estimates to train agents via offline reinforcement learning (RL). By grounding LLM-generated estimates to the target environment domain, CoREN enables effective offline learning of embodied agents in different environments. Experimental results on the VirtualHome benchmark demonstrate that CoREN outperforms other offline RL agents and achieves comparable performance to state-of-the-art LLM-based agents, despite using LLMs only for training and having significantly fewer parameters.
Using Large Language Models to Enhance Embodied Agent Learning: Introducing CoREN
Employing large language models (LLMs) in the field of embodied agent learning has gained traction in recent years. However, despite their potential, direct utilization of LLMs as agents presents several limitations in practical applications. In this article, we propose an alternative approach that harnesses the power of LLMs to enhance the training of separate agents via offline reinforcement learning (RL).
The core idea behind our approach, which we refer to as the consistency-guided reward ensemble framework (CoREN), is to leverage LLMs as tools for providing dense reward feedback on individual actions in training datasets. By utilizing an LLM in this manner, we aim to alleviate the challenges associated with grounding LLM-generated estimates to the target environment domain.
The CoREN Framework: Deriving Domain-grounded Rewards
The CoREN framework is designed to address the difficulties in grounding LLM-generated estimates by employing an adaptive ensemble of spatio-temporally consistent rewards. These rewards are derived from the LLM’s feedback and serve to provide domain-grounded rewards in the training datasets, enabling effective offline learning of embodied agents in diverse environment domains.
Unlike traditional RL approaches that rely solely on predefined rewards or human feedback, CoREN leverages the capabilities of LLMs to generate rich, context-specific reward signals. By incorporating an adaptive ensemble, the framework ensures that the rewards remain consistent across time and space, further aiding the agents in learning the dynamics of diverse environments.
Experiments and Results
To evaluate the effectiveness of CoREN, we conducted experiments using the VirtualHome benchmark, a widely adopted evaluation platform for embodied agent learning. Our results demonstrate that CoREN significantly outperforms other offline RL agents in terms of learning performance.
Furthermore, despite using LLMs only for training and having a substantially smaller policy network (117M parameters compared to 8B parameters in state-of-the-art LLM-based agents), CoREN achieves comparable performance. This highlights the potential of leveraging the strengths of LLMs as tools for embodied agent learning rather than relying on them as direct agents themselves.
Innovation and Future Directions
The CoREN framework introduces a novel approach to utilizing LLMs in the training of embodied agents. By separating the role of LLMs as tools for reward feedback from the role of the agents themselves, we overcome some of the limitations associated with direct LLM utilization.
In future work, we aim to explore the scalability of CoREN by investigating the performance of the framework with larger LLM architectures. Additionally, we plan to extend the framework to incorporate online reinforcement learning, enabling agents to adapt and learn in real-time environments.
By leveraging the power of LLMs within the CoREN framework, we can enhance the training of embodied agents and pave the way for more efficient and effective AI-driven systems in various domains.
The paper titled “Consistency-Guided Reward Ensemble Framework for Training Embodied Agents using Large Language Models” introduces a novel approach to training embodied agents by leveraging large language models (LLMs) as tools rather than directly using them as agents. The authors address the limitations of using LLMs as agents and propose a framework called CoREN, which utilizes LLMs to provide dense reward feedback for individual actions in training datasets.
One of the challenges in using LLMs for training embodied agents is the difficulty in grounding LLM-generated estimates to the target environment domain. The CoREN framework tackles this issue by employing an adaptive ensemble of spatio-temporally consistent rewards. By deriving domain-grounded rewards in the training datasets, CoREN enables effective offline learning of embodied agents in different environment domains.
To evaluate the effectiveness of CoREN, the authors conducted experiments using the VirtualHome benchmark. The results demonstrate that CoREN outperforms other offline reinforcement learning (RL) agents and achieves comparable performance to state-of-the-art LLM-based agents with 8 billion parameters, despite CoREN having only 117 million parameters for the agent policy network and using LLMs solely for training.
This research is significant as it provides a novel approach to training embodied agents using LLMs. By utilizing LLMs as tools for providing reward feedback, CoREN addresses the limitations of directly employing LLMs as agents. This approach has the potential to enhance the performance and generalization of embodied agents in different environment domains.
Moving forward, it would be interesting to see how the CoREN framework can be further improved and extended. One potential direction could be exploring the use of larger LLMs and investigating their impact on the performance of embodied agents. Additionally, it would be valuable to apply the CoREN framework to real-world scenarios and evaluate its effectiveness in practical applications. Overall, this work opens up new possibilities for training embodied agents and paves the way for future research in this area.
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