Pre-trained Language Models Improve the Few-shot Prompt Ability of Decision Transformer (2408.01402v1)

Abstract: Decision Transformer (DT) has emerged as a promising class of algorithms in offline reinforcement learning (RL) tasks, leveraging pre-collected datasets and Transformer's capability to model long sequences. Recent works have demonstrated that using parts of trajectories from training tasks as prompts in DT enhances its performance on unseen tasks, giving rise to Prompt-DT methods. However, collecting data from specific environments can be both costly and unsafe in many scenarios, leading to suboptimal performance and limited few-shot prompt abilities due to the data-hungry nature of Transformer-based models. Additionally, the limited datasets used in pre-training make it challenging for Prompt-DT type of methods to distinguish between various RL tasks through prompts alone. To address these challenges, we introduce the LLM-initialized Prompt Decision Transformer (LPDT), which leverages pre-trained LLMs for meta-RL tasks and fine-tunes the model using Low-rank Adaptation (LoRA). We further incorporate prompt regularization to effectively differentiate between tasks based on prompt feature representations. Our approach integrates pre-trained LLM and RL tasks seamlessly. Extensive empirical studies demonstrate that initializing with a pre-trained LLM significantly enhances the performance of Prompt-DT on unseen tasks compared to baseline methods.

• The paper introduces LPDT, a Language model-initialized Prompt Decision Transformer, which leverages pre-trained language models to significantly improve few-shot prompt ability in offline RL tasks.
• The approach uses LoRA fine-tuning and prompt regularization to efficiently integrate pre-trained language model capabilities and improve task differentiation.
• Empirical results show LPDT outperforms baselines on MuJoCo and Meta World tasks, demonstrating potential for data-scarce applications like robotics and autonomous driving.

Overview of "Pre-trained LLMs Improve the Few-shot Prompt Ability of Decision Transformer"

The paper "Pre-trained LLMs Improve the Few-shot Prompt Ability of Decision Transformer" presents an innovative approach to enhancing the performance of Decision Transformers (DT) in offline reinforcement learning (RL) tasks. The authors propose leveraging pre-trained LLMs to address existing limitations in few-shot prompt abilities of DTs, especially in scenarios constrained by limited datasets due to potentially costly or unsafe data collection environments.

Key Contributions

1. Introduction of LPDT: By introducing the LLM-initialized Prompt Decision Transformer (LPDT), the authors make a significant contribution towards enhancing few-shot learning in offline RL tasks. The LPDT extends DT's architecture by integrating pre-trained LLMs, enabling better task differentiation and thus improving performance on unseen tasks.
2. Low-rank Adaptation (LoRA) Fine-tuning: The research utilizes Low-rank Adaptation (LoRA) methods to efficiently fine-tune the model. This approach updates only a small subset of parameters, minimizing computational overhead while retaining the benefits of pre-trained weights from LLMs.
3. Prompt Regularization: To further improve task differentiation, the paper introduces prompt regularization. This involves imposing constraints on prompt feature representations, thus aiding in effective task recognition and action generation for new tasks. Both supervised and unsupervised techniques are explored for this purpose.

Empirical Evaluation

The authors conduct extensive experiments within MuJoCo control environments and Meta World ML1 tasks to validate their approach. Results indicate that LPDT, initialized with pre-trained LLMs like DistilGPT2, significantly outperforms existing methods such as Prompt-DT and Prompt-Tuning DT, especially in cumulative rewards on unseen tasks.

• Cheetah-dir and Cheetah-vel: LPDT demonstrates superior performance compared to baseline methods, indicating an effective translation of pre-trained LLM capabilities to RL tasks.
• Ant-dir Tasks: While LPDT provides competitive results, there remains room for enhancement in future iterations.
• Meta World Tasks: Highlighted challenges in these complex tasks suggest that while LPDT is beneficial, further integration of more sophisticated LLM techniques may be required for even better performance.

Implications and Future Directions

The integration of pre-trained LLMs into RL presents a promising direction for future AI developments. LPDT exemplifies a paradigm where LLM capabilities are transferred to decision-making tasks, reducing the dependency on large datasets and enhancing task adaptability.

• Practical Implications: This approach can revolutionize applications in autonomous driving and robotic manipulation, where data acquisition can be hazardous or costly. By reducing reliance on extensive datasets, businesses and research can more feasibly apply these models.
• Theoretical Implications: The work opens avenues for further paper on how LLM training paradigms can be aligned more closely with RL objectives. Future models could expand on integrating even larger or more diverse LLMs for richer task comprehension.
• Speculation on Future Developments: As computational capabilities and the diversity of pre-trained LLMs grow, the intersection of natural language processing and decision-making tasks in AI will likely become more sophisticated. Exploration of architectures that seamlessly blend the two fields could reshape the landscape of offline RL.

In summary, this paper effectively demonstrates how pre-trained LLMs can be leveraged to enhance few-shot learning capabilities in decision transformers, providing a significant boost to the adaptability and efficiency of RL agents in practical applications. The LPDT framework stands as a crucial step towards integrating meta-RL tasks and LLM pre-training, promising impactful advancements in the field.