Zero-shot Model-based Reinforcement Learning using Large Language Models (2410.11711v1)

Abstract: The emerging zero-shot capabilities of LLMs have led to their applications in areas extending well beyond natural language processing tasks. In reinforcement learning, while LLMs have been extensively used in text-based environments, their integration with continuous state spaces remains understudied. In this paper, we investigate how pre-trained LLMs can be leveraged to predict in context the dynamics of continuous Markov decision processes. We identify handling multivariate data and incorporating the control signal as key challenges that limit the potential of LLMs' deployment in this setup and propose Disentangled In-Context Learning (DICL) to address them. We present proof-of-concept applications in two reinforcement learning settings: model-based policy evaluation and data-augmented off-policy reinforcement learning, supported by theoretical analysis of the proposed methods. Our experiments further demonstrate that our approach produces well-calibrated uncertainty estimates. We release the code at https://github.com/abenechehab/dicl.

Zero-shot Model-based Reinforcement Learning using LLMs

The paper explores novel methodologies for employing LLMs in Reinforcement Learning (RL), focusing on two primary tasks: model-based policy evaluation and data-augmented off-policy RL. The authors propose an approach termed Disentangled In-Context Learning (DICL) to tackle the challenges of using LLMs with continuous state spaces in Markov Decision Processes (MDPs). The paper provides analytical insights and practical applications of integrating LLMs into RL environments.

Methodological Contributions

The authors introduce DICL to address two fundamental challenges: 1) the integration of action information into LLM context and 2) handling the interdependence between state-action dimensions. DICL leverages Principal Component Analysis (PCA) to disentangle these interdependencies, projecting state-action vectors into an independent latent space to facilitate the application of In-Context Learning (ICL).

The methodology involves using ICL to predict Markovian state transitions by processing multivariate trajectories. The proposed DICL method reduces the complexity of learning dynamics by capturing essential dependencies between state and action pairs, thus enabling efficient use of LLMs for predicting future states in RL environments.

Theoretical Insights

The paper extends the framework of Model-Based Policy Optimization (MBPO) by introducing a multi-branch rollout mechanism. The authors derive a novel return bound for policies learned under LLM-based multi-branch rollouts, highlighting the relationship between key parameters such as the context length $T$, prediction horizon $k$, and generalization error $\varepsilon$. This bound provides a theoretical guarantee of the LLM's efficacy in approximating true environment dynamics under specified conditions, thus guiding the design and application of such models in RL tasks.

Empirical Evaluations

Empirical results presented in the paper showcase the application of DICL across various environments, including MuJoCo's HalfCheetah system. DICL demonstrates a reduction in multi-step prediction error compared to both a vanilla ICL approach and baseline models trained on historic data. The computational efficiency of PCA-based dimensionality reduction further enhances DICL's practical utility.

In data-augmented off-policy RL, the proposed DICL-SAC algorithm shows improved sample efficiency over standard SAC, especially in the initial phases of learning. This demonstrates the potential of augmenting real interactions with high-quality LLM-generated synthetic transitions, thereby accelerating the learning process without sacrificing performance.

Calibration and Uncertainty

The paper also investigates the calibration of uncertainty estimates from LLM outputs. The results suggest that LLM-based models provide well-calibrated probabilistic predictions, which is crucial for robust decision-making in model-based RL settings. Such calibration allows for more informed policy evaluations and risk assessments, contributing to the reliability and robustness of the RL systems developed.

Implications and Future Directions

The integration of LLMs into RL frameworks has promising implications. It offers a pathway to enhance model-based approaches by leveraging LLM's pre-trained capabilities and in-context learning phenomena. The ability to handle complex state-action dynamics and provide calibrated uncertainty estimates broadens the scope for LLMs in decision-making tasks beyond textual domains.

Future research could explore deeper integration of LLMs with advanced disentanglement methods, such as Variational Autoencoders, for richer representation learning in RL. Additionally, investigating the scalability of these methods with larger models and diverse RL environments could further solidify the role of LLMs in real-world applications. The paper lays foundational work for such explorations, representing significant progress in model-based reinforcement learning methodologies.