Pushing RL Boundaries: Integrating Foundational Models, e.g. LLMs and VLMs, into Reinforcement Learning

This post discusses how pre-trained foundational models such as Large Language Models (LLMs) and Vision-Language Models (VLMs) can enhance the capabilities of reinforcement learning algorithms. It explores the potential applications of foundational models in the environment, state representation, policy, and reward generation. The post also highlights the challenges in training RL agents with foundational policies and the benefits of leveraging foundational models for enhanced understanding of RL policies.