Unsupervised Reinforcement Learning with Foundation Models

Reinforcement learning (RL) is a subfield of machine learning that focuses on training agents to make decisions in complex environments. While RL has shown great promise, it often requires extensive human supervision, which can be time-consuming and costly. In this article, we propose using foundation models (FMs) to train RL agents without human supervision. FMs are large language models that have been pre-trained on a massive corpus of text data and have learned to generate coherent and contextually relevant text. By leveraging these pre-trained models, we can train RL agents to perform tasks in various environments without explicit reward signals.
Our approach is motivated by the observation that FMs can be used to generate meaningful captions for images and videos. We propose using FMs to learn visual feedback for RL agents, which enables them to adapt to new environments without additional training data. Our method integrates FMs with existing RL frameworks, allowing agents to learn tasks such as combat, growth, and digging.
We conduct extensive qualitative analysis to evaluate the quality of the learned behavior and identify limitations of our approach. We also perform comparative studies to show the effectiveness of our method relative to existing unsupervised RL methods. Our results demonstrate that FMs can significantly improve the performance of RL agents in various environments.
Our work has important implications for developing practical RL systems that can adapt to new environments without explicit reward signals. By leveraging pre-trained language models, we can reduce the need for human supervision and enable RL agents to learn tasks more efficiently. Our approach has applications in areas such as robotics, autonomous vehicles, and game AI.
In summary, this article proposes using foundation models to train reinforcement learning agents without human supervision. By leveraging pre-trained language models, we can enable RL agents to learn tasks more efficiently and adapt to new environments more quickly. Our approach has important implications for developing practical RL systems that can be applied in various domains.