Multi-Task Learning for Heterogeneous Prediction from Video Game State with Transfer Learning

Abstract:Multi-task learning (MTL) is a promising approach for prediction tasks derived from video game state data, as modern game telemetry provides multiple related supervision signals from the same structured observations. We study whether a shared model trained jointly across tasks in team-based multiplayer games can improve generalization while reducing training and inference cost compared to specialized single-task models. We adapt a multimodal architecture for endpoint prediction to a general multi-task setting that combines rasterized vision inputs, global match context, and per-unit state information through an image encoder and attention-based interaction modeling. Experiments on a large proprietary World of Tanks dataset compare single-task and multi-task training, evaluate weighting strategies for mixed losses and conflicting gradients, and test pre-training/fine-tuning under limited target-data regimes. We also examine within-game transfer across game maps under structured environment shift.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.21290 [cs.LG]

(or arXiv:2607.21290v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.21290 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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