Can Wikipedia Help Offline Reinforcement Learning? (2201.12122v3)

Abstract: Fine-tuning reinforcement learning (RL) models has been challenging because of a lack of large scale off-the-shelf datasets as well as high variance in transferability among different environments. Recent work has looked at tackling offline RL from the perspective of sequence modeling with improved results as result of the introduction of the Transformer architecture. However, when the model is trained from scratch, it suffers from slow convergence speeds. In this paper, we look to take advantage of this formulation of reinforcement learning as sequence modeling and investigate the transferability of pre-trained sequence models on other domains (vision, language) when finetuned on offline RL tasks (control, games). To this end, we also propose techniques to improve transfer between these domains. Results show consistent performance gains in terms of both convergence speed and reward on a variety of environments, accelerating training by 3-6x and achieving state-of-the-art performance in a variety of tasks using Wikipedia-pretrained and GPT2 LLMs. We hope that this work not only brings light to the potentials of leveraging generic sequence modeling techniques and pre-trained models for RL, but also inspires future work on sharing knowledge between generative modeling tasks of completely different domains.

• The paper demonstrates that pre-trained language models can effectively transfer Wikipedia-derived features to boost offline reinforcement learning.
• It introduces innovative transfer techniques, including extended positional embeddings and similarity objectives, to repurpose sequence models for RL tasks.
• Empirical results on Gym and Atari benchmarks show state-of-the-art performance with 3-6x faster convergence compared to standard Decision Transformers.

Leveraging Pre-trained LLMs for Offline Reinforcement Learning

The paper "Can Wikipedia Help Offline Reinforcement Learning?" explores the transferability of pre-trained sequence models from domains such as language and vision to offline reinforcement learning (RL) tasks. The authors, Machel Reid, Yutaro Yamada, and Shixiang Shane Gu, attempt to address the challenges faced in fine-tuning RL models by leveraging sequence modeling techniques popularized in natural language processing.

Overview and Contributions

The research investigates whether pre-trained LLMs, specifically those based on Transformer architectures, can be adapted to offline RL tasks, including control and games. The paper leverages the analogy between sequence modeling and RL, proposing that methodologies in one domain might enhance task performance in another. With finite neural resources, pre-trained models can potentially reduce computational demands during fine-tuning.

The paper's methodological innovations include:

• Transfer Techniques: Developing techniques like the extension of positional embeddings and encouraging embedding similarity to maximise the utility of features learned from LLMs in RL tasks.
• Training Efficiency: Demonstrating substantial improvements in convergence speed and policy performance when pre-training a model using generic sequence modeling techniques. The models improve training time by a factor of 3-6x compared to vanilla Decision Transformers.
• Model Variants: Evaluating models pre-trained on both language and vision datasets (e.g., GPT-2 and CLIP) to understand the unique contributions of different types of pre-training to RL performance.

Experimental Results

The research provides an extensive empirical evaluation using benchmark datasets from D4RL for OpenAI Gym MuJoCo and Atari tasks. Key numerical results include:

• The language-pre-trained models achieve state-of-the-art performance on both Gym and Atari datasets, outperforming strong baselines such as the Decision Transformer (DT) by significant margins.
• Pre-training models on language data consistently improves over DT, especially observable in OpenAI Gym's Medium-Expert setting, where pre-trained models average performance scores of 78.3 and 80.1 (normalized), outperforming DT’s 74.7.
• The use of LLM co-training and a similarity-based objective further enriches this transferability.

Theoretical and Practical Implications

The paper posits several implications for the theoretical and practical landscape of AI:

• Cross-Domain Transferability: The findings underscore the surprising efficacy of LLMs in RL tasks, suggesting a universal structural similarity in sequence modeling tasks across domains.
• Efficient Computation: This research hints at substantial computational efficiency, showcasing how transfer learning can drastically reduce time-to-convergence for complex RL models.
• Pre-training Paradigms: This work pioneers the conversation on leveraging pre-training as a default strategy, not just in RL but potentially other domains where sequence modeling is prevalent.

Future Directions

The research opens several avenues for future inquiry:

• Exploration of Larger Models: Larger-scale models and datasets might yield further insights into transferability and performance gains.
• Long-range Dependencies: Investigating the specific role and limitations of long-range context and attention mechanisms in offline RL tasks.
• Complex Sequential Domains: Extending similar paradigms beyond pure language or visual inputs to include tasks involving multimodal inputs.

In conclusion, the paper provides a rigorous exploration into the potential of applying pre-trained sequence models like Transformers to offline RL tasks, with promising results that position pre-trained LLMs as a viable, efficient strategy for enhancing RL performance.