Vision-Language Models as a Source of Rewards

Kate Baumli, Satinder Baveja,Feryal Behbahani,Harris Chan, Gheorghe Comanici,Sebastian Flennerhag,Maxime Gazeau, Kristian Holsheimer,Dan Horgan, Michael Laskin, Clare Lyle, Hussain Masoom, Kay McKinney,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 Zhang


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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.
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