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

• URL: http://arxiv.org/abs/2607.21290v1
• Date: Thu, 23 Jul 2026 13:12:51 GMT
• Title: Multi-Task Learning for Heterogeneous Prediction from Video Game State with Transfer Learning
• Abstract summary: Multi-task learning is a promising approach for prediction tasks derived from video game state data.<n>We study whether a shared model trained jointly across tasks in team-based multiplayer games can improve generalization.
• Score: 41.132061576291065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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