@inproceedings{
chan2023visionlanguage,
title={Vision-Language Models as a Source of Rewards},
author={Harris Chan and Volodymyr Mnih and Feryal Behbahani and Michael Laskin and Luyu Wang and Fabio Pardo and Maxime Gazeau and Himanshu Sahni and Dan Horgan and Kate Baumli and Yannick Schroecker and Stephen Spencer and Richie Steigerwald and John Quan and Gheorghe Comanici and Sebastian Flennerhag and Alexander Neitz and Lei M Zhang and Tom Schaul and Satinder Singh and Clare Lyle and Tim Rockt{\"a}schel and Jack Parker-Holder and Kristian Holsheimer},
booktitle={Second Agent Learning in Open-Endedness Workshop},
year={2023},
url={https://openreview.net/forum?id=Xw1hVTWxxQ}
}
Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning. A key limiting factor for building generalist agents with RL has been the need for a large number of reward functions for achieving different goals. We investigate the feasibility of using off-the-shelf vision-language models, or VLMs, as sources of rewards for reinforcement learning agents. We show how rewards for visual achievement of a variety of language goals can be derived from the CLIP family of models, and used to train RL agents that can achieve a variety of language goals. We showcase this approach in two distinct visual domains and present a scaling trend showing how larger VLMs lead to more accurate rewards for visual goal achievement, which in turn produces more capable RL agents.
Vision-Language Models as a Source of Rewards
Kate Baumli Satinder Singh Feryal Behbahani Harris Chan Gheorghe Comanici Sebastian Flennerhag Maxime Gazeau Kristian Holsheimer Dan Horgan Michael Laskin Clare Lyle Volodymyr Mnih Alexander Neitz Fabio Pardo Jack Parker-Holder John Quan Tim Rocktäschel Himanshu Sahni Tom Schaul Yannick Schroecker Stephen Spencer Richie Steigerwald Luyu Wang Lei M Zhang