akhaliq
Abstract
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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this is not the page where you can try the model guys, this is a research paper. 😅
@akhaliq
sorry to bother you, but I noticed quite a few papers getting comments from people thinking that this is a place to try models, Unfortunately, this confusion seems to be growing and I think/suggest that there might be a need to add a little disclosure to make sure everyone understands that this is not the place to test the models.
for example, 2 pages on 15-Dec-2023 had these types of comments one of them being this and the other one is here
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