aiqbench analysis
This research proposes using Vision Language Models (VLMs) to annotate video game datasets with human-defined rewards, thereby simplifying the reward engineering process required for Reinforcement Learning in offline settings. By automating the extraction of these signals, the method aims to mitigate common RL challenges such as sparse rewards and difficult trial-and-error tuning without needing direct access to a live game engine. The study highlights both the potential efficiency gains and current limitations when applying VLMs to conditioned agent training within gaming environments.
Training a Conditioned Video Game Agent on a VLM Annotated Dataset
arXiv:2608.05954v1 Announce Type: cross Abstract: Reinforcement Learning (RL) is a powerful but far from easy-to-use technique for policy learning. In the specific case of video games, access to the game engine is required to get rewards for training (e.g. to collect rewards from the environment). Furthermore, the proper identification and weighting of the rewards generally requires a difficult trial-and-error approach. Lastly, rewards are often sparse and understanding how they eventually affect the learned policy is a non-trivial exercise. To ease these issues we propose annotating a video game dataset with Vision Language Models (VLMs) instructed to extract human defined rewards. We show that offline RL can then be used to train a conditioned agent that responds accordingly to the desired returns and we discuss the difficulties and limitations that emerged in our early experiments.
Abstract:Reinforcement Learning (RL) is a powerful but far from easy-to-use technique for policy learning. In the specific case of video games, access to the game engine is required to get rewards for training (e.g. to collect rewards from the environment). Furthermore, the proper identification and weighting of the rewards generally requires a difficult trial-and-error approach. Lastly, rewards are often sparse and understanding how they eventually affect the learned policy is a non-trivial exercise. To ease these issues we propose annotating a video game dataset with Vision Language Models (VLMs) instructed to extract human defined rewards. We show that offline RL can then be used to train a conditioned agent that responds accordingly to the desired returns and we discuss the difficulties and limitations that emerged in our early experiments.
Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2608.05954 [cs.AI]
(or arXiv:2608.05954v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.05954 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 232 words
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