Hierarchical Reinforcement Learning in StarCraft Micromanagement with Influence Maps and Cluster-based Scripts
arXiv:2606.30092v1 Announce Type: new Abstract: Real-time strategy (RTS) games present significant AI challenges, characterized by expansive state-action spaces arising from multi-unit coordination in continuous battlefields, and sparse delayed rewards stemming from final win/lose signals. Existing approaches face a trade-off between managing the dimensionality explosion of joint actions and maintaining the interpretability of complex state representations. This complexity is further intensified by the limitation of traditional hierarchical structures in adaptively decomposing tasks into effec
This research is published as AI capabilities in strategic game environments like StarCraft continue to push the boundaries of reinforcement learning and hierarchical task decomposition.
Advanced AI techniques demonstrated in complex simulations like StarCraft micromanagement have direct implications for real-world autonomous systems, particularly in areas requiring multi-agent coordination and adaptive strategy.
This research refines AI's ability to manage high-dimensional action spaces and complex state representations in multi-unit scenarios, potentially leading to more robust and generalized autonomous agents.
- · AI research institutions
- · Robotics and automation sector
- · Defence technology developers
- · Traditional rule-based AI approaches
- · Human domain experts in complex task scheduling
Improved performance of AI agents in complex, dynamic, and multi-unit environments.
Accelerated development of autonomous systems for logistics, disaster response, and strategic operations.
Ethical and regulatory discussions around the deployment of highly autonomous and adaptable AI systems in sensitive contexts.
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Read at arXiv cs.AI