LLMs-augmented Contextual Bandit
This work addresses the problem of decision-making in complex environments for reinforcement learning practitioners, though it appears incremental as it combines existing LLMs with a known framework.
The paper tackles the challenge of contextual bandits struggling with complex contexts by integrating large language models (LLMs) as encoders to enrich context representations. Preliminary results on synthetic datasets show notable improvements in cumulative rewards and reductions in regret compared to traditional bandit algorithms.
Contextual bandits have emerged as a cornerstone in reinforcement learning, enabling systems to make decisions with partial feedback. However, as contexts grow in complexity, traditional bandit algorithms can face challenges in adequately capturing and utilizing such contexts. In this paper, we propose a novel integration of large language models (LLMs) with the contextual bandit framework. By leveraging LLMs as an encoder, we enrich the representation of the context, providing the bandit with a denser and more informative view. Preliminary results on synthetic datasets demonstrate the potential of this approach, showing notable improvements in cumulative rewards and reductions in regret compared to traditional bandit algorithms. This integration not only showcases the capabilities of LLMs in reinforcement learning but also opens the door to a new era of contextually-aware decision systems.