Researchers have developed a framework called Language Models for Motion Control (LaMo) that incorporates Large Language Models (LLMs) for offline reinforcement learning. LaMo combines pre-trained LLMs with Decision Transformers (DT) and introduces innovations like LoRA fine-tuning and auxiliary language loss. It outperforms existing methods in sparse-reward tasks and narrows the gap between value-based offline RL and decision transformers in dense-reward tasks. The study highlights the effectiveness of the LaMo framework and suggests further exploration of larger LLMs in offline RL.
Introducing LaMo: Language Models for Motion Control
Researchers have developed a framework called Language Models for Motion Control (LaMo) that leverages Large Language Models (LLMs) for offline reinforcement learning (RL). LaMo combines pre-trained LLMs with Decision Transformers (DT) to enhance RL policy learning. It outperforms existing methods in tasks with sparse rewards and narrows the gap between value-based offline RL and decision transformers in tasks with dense rewards. LaMo is particularly effective in scenarios with limited data samples.
How LaMo Works
LaMo utilizes pre-trained LLMs and DTs to enhance representation learning. It incorporates innovations like LoRA fine-tuning, non-linear MLP projections, and auxiliary language loss. By reframing RL as a conditional sequence modeling problem, LaMo achieves superior performance in sparse-reward tasks and reduces the performance gap between value-based and DT-based methods in dense-reward scenarios.
Evaluating LaMo
Extensive experiments have been conducted to assess LaMo’s performance across various tasks and environments. The framework has been compared to strong RL baselines like CQL, IQL, TD3BC, BC, DT, and Wiki-RL. LaMo consistently outperforms these baselines in both sparse and dense-reward tasks, demonstrating its robust learning ability and avoiding overfitting. Evaluation of the D4RL benchmark and thorough ablation studies further confirm the effectiveness of each component within the framework.
Limitations and Future Exploration
While LaMo shows promising results, there are areas for further exploration. In-depth exploration of higher-level representation learning techniques is needed to enhance the generalizability of full fine-tuning. Computational constraints have limited the examination of alternative approaches like joint training. Additionally, the impact of varying pre-training qualities of LMs beyond the models used in the study needs to be addressed.
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