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<p>Complex team sports require sophisticated, rapid-dynamic decision-making schemas that adjust to tactical situations and adversarial behaviors. This makes tactically cohesive, field-applicable policies difficult. TACT-RLNet, a modular reinforcement learning framework based on context-based tactical concepts for active coordination and real-time adaptation in multi-agent team sports, addresses these issues. Five key modules comprise TACT-RLNet. (1) CMATA-credited attribution specificity by counterfactually calculating each player’s reward alignment and accountability contribution; (2) Phase-Aware Tactical Encoder (PATE) captures strategic game phases like attacking or defending and embeds tactical context to improve policy coherence under temporal shift (3) Opponent Tactical Adaptation Layer (OTAL)—real-time data-driven strategy model upgrades and progressive policy modification; 4. Communication-Constrained Curriculum Trainer (C3T)—creates real conditions for training policy under limited communication constraint for good coordination; and 5. Spatial Threat Mapping and Reward Shaping Engine (STMRSE)—pressure zone modeling and reward shaping based on spatial influence to support behavior organization like marking and pressing Performance enhanced 25% faster training convergence, 23% more responsiveness to opponent techniques, 17% better coordination under communication limits, and 33% stronger spatial tactical control with the integrated architecture. TACT-RLNet becomes the top learning-based tactical modeling for team sports with analytical rigor and practical constraints.</p>

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Reinforcement learning strategies in game tactics and real time decision making for team sport

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月19日

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  • Zhihui Lai,
  • Haytham F. Isleem,
  • Vikrant S. Vairagade,
  • Rajanikanth Aluvalu,
  • Ghanshyam G. Tejani,
  • Mohamed Sharaf

摘要

Complex team sports require sophisticated, rapid-dynamic decision-making schemas that adjust to tactical situations and adversarial behaviors. This makes tactically cohesive, field-applicable policies difficult. TACT-RLNet, a modular reinforcement learning framework based on context-based tactical concepts for active coordination and real-time adaptation in multi-agent team sports, addresses these issues. Five key modules comprise TACT-RLNet. (1) CMATA-credited attribution specificity by counterfactually calculating each player’s reward alignment and accountability contribution; (2) Phase-Aware Tactical Encoder (PATE) captures strategic game phases like attacking or defending and embeds tactical context to improve policy coherence under temporal shift (3) Opponent Tactical Adaptation Layer (OTAL)—real-time data-driven strategy model upgrades and progressive policy modification; 4. Communication-Constrained Curriculum Trainer (C3T)—creates real conditions for training policy under limited communication constraint for good coordination; and 5. Spatial Threat Mapping and Reward Shaping Engine (STMRSE)—pressure zone modeling and reward shaping based on spatial influence to support behavior organization like marking and pressing Performance enhanced 25% faster training convergence, 23% more responsiveness to opponent techniques, 17% better coordination under communication limits, and 33% stronger spatial tactical control with the integrated architecture. TACT-RLNet becomes the top learning-based tactical modeling for team sports with analytical rigor and practical constraints.