- The paper introduces LaMo, a framework that integrates pre-trained language models with Decision Transformers to tackle offline RL challenges with scarce in-domain data.
- It employs LoRA fine-tuning and non-linear MLP embeddings to efficiently adapt pre-trained LM features while preserving inherent language understanding through an auxiliary loss.
- LaMo achieves state-of-the-art results in both sparse- and dense-reward tasks, demonstrating robust few-shot learning in applications such as Kitchen and Atari domains.
Unleashing the Power of Pre-trained LLMs for Offline Reinforcement Learning
This paper addresses the challenging task of Offline Reinforcement Learning (RL), which involves developing an optimal policy using pre-collected datasets without the option of further data collection. The current methodology faces limitations, especially when in-domain data is scarce. To tackle these challenges, the authors propose a framework named LLMs for Motion Control (LaMo), which leverages pre-trained LLMs (LMs) in combination with Decision Transformers to enhance the performance in offline RL tasks.
The LaMo framework consists of four key components. First, it initializes Decision Transformers with sequentially pre-trained LMs. Second, it employs the Low-Rank Adaptation (LoRA) fine-tuning method. LoRA is highlighted as a less resource-intensive alternative in contrast to full-weight fine-tuning, allowing effective integration of pre-trained LM knowledge with domain-specific data. Third, it uses non-linear Multi-Layer Perceptrons (MLPs) instead of linear projections for embeddings, enhancing the representation learning capacity. Fourth, an auxiliary language prediction loss is integrated during fine-tuning, which stabilizes LMs and maintains their inherent language understanding abilities.
Empirical evaluations reveal that the LaMo framework achieves state-of-the-art performance in various sparse-reward tasks and narrows the gap between value-based offline RL methods and decision transformers in dense-reward tasks. Notably, LaMo displays superior performance under limited data scenarios, a testament to the few-shot learning capabilities inherited from the LMs. For instance, LaMo outperforms other approaches in the Kitchen and Atari domains, where rewards are either sparse or dense, making it particularly adept at handling diverse reward structures.
In terms of implications, the research indicates a promising direction for leveraging pre-trained LLMs beyond traditional NLP tasks and applying them to motion control problems in reinforcement learning. This could potentially ease the computational burden and data requirements by utilizing the few-shot learning capabilities of LMs, thereby broadening the applicability of RL techniques in real-world settings where data acquisition is costly or risky. The paper suggests that future work could explore utilizing larger LLMs or integrating more sophisticated prompt engineering techniques to further harness the language reasoning ability of these models.
The findings demonstrate the feasibility and benefits of cross-domain pre-training and adaptation, suggesting that the integration of LMs could be a fruitful line of inquiry in advancing RL methodologies. Furthermore, this cross-pollination of techniques from NLP to RL could drive new innovations and insights essential for tackling the complexities inherent in offline RL tasks.