PursuitVLA: An Executable Vision–Language–Action Policy for Cross-Map Zero-Shot Pursuit–Evasion Games

Pursuit–evasion games require pursuers to coordinate over long adversarial interactions and complex map geometry while producing executable actions. Existing graph-based methods depend on map-specific representations, while vision–language agents are predominantly designed for single-agent tasks, making coordinated and executable multi-agent action generation difficult. We introduce PursuitVLA, an autoregressive vision–language–action policy that generates reasoning traces and executable waypoint tokens from top-down observations and multi-agent states, conditioning each pursuer on previously generated teammate actions. The same role-conditioned policy also operates as an evader. Training first uses dynamic-programming (DP) demonstrations with strategic reasoning and then applies reinforcement fine-tuning with Sequential Action Compensation (SeqAC), which scores candidate actions at intermediate states induced by policy-generated teammate prefixes, allowing later agents to compensate for deviations in earlier teammate actions. Without retraining or map-specific graph reconstruction, PursuitVLA achieves 79.6% capture against DP evaders on eight unseen maps, compared with 63.0% for EPG, the strongest graph-learning baseline. It remains effective under local map perturbations and transfers from 2v1 training to simulated 10v1 pursuit. Without additional training, the same policy controls two pursuers in a closed-loop outdoor deployment, achieving an 80% capture rate (16/20 trials) against a DP-controlled evader, compared with 50% (10/20 trials) for EPG. Our project is available at: https://sites.google.com/view/pursuitagent