arXiv AI June 30, 2026 By Chunhui Bai, Changhe Li, Dequan Li, Xinye Cai, Shengxiang Yang

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 30

SAT-RTS: A systematic framework for tactical knowledge extraction and visualization-based analysis in real-time strategy games

arXiv:2606. 30090v1 Announce Type: new Abstract: Efficient tactical knowledge extraction and analysis in real-time strategy (RTS) games micromanagement are constrained by the high-dimensional coupled state-action sequential data and the black-box decision-making process.

By Chunhui Bai, Changhe Li, Yuqiang Li, Lei Liu, Shoufei Han

safety

arXiv AI

Jul 22

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal

arXiv:2607. 18296v1 Announce Type: new Abstract: Baghchal is a two-player asymmetric board game with Nepali origins where four tigers are to capture goats and twenty goats desire to keep tigers in immobility.

By Ranjit Raut, Aarav Subedi, Sagun Rai, Aaryan Shakya, Manoj Shakya

reinforcement-learning

arXiv AI

Jun 19

Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution

arXiv:2606. 20014v1 Announce Type: cross Abstract: Reinforcement learning (RL) has achieved strong performance in sequential decision-making, yet scaling to complex multi-agent environments remains challenging due to sparse rewards, large state-action spaces, and the difficulty of learning coordinated strategies.

By Jannik H\"osch, Alessandro Sestini, Florian Fuchs, Amir Baghi, Joakim Bergdahl, Konrad Tollmar, Jean-Philippe Barrette-LaPierre, Linus Gissl\'en

llms agents reinforcement-learning

arXiv Machine Learning

Jul 21

Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials

arXiv:2105. 00990v3 Announce Type: replace Abstract: Autonomous control in high-dimensional, continuous state spaces is a persistent and important challenge in the fields of robotics and artificial intelligence.

By Adrian P. Pope, Jaime S. Ide, Daria Micovic, Henry Diaz, David Rosenbluth, Lee Ritholtz, Jason C. Twedt, Thayne T. Walker, Kevin Alcedo, Daniel Javorsek

agents reinforcement-learning robotics

arXiv Machine Learning

Jun 18

Hierarchical Planning with Latent World Models

arXiv:2604. 03208v2 Announce Type: replace Abstract: World models are a promising path to zero-shot embodied control through planning.

By Wancong Zhang, Basile Terver, Artem Zholus, Soham Chitnis, Harsh Sutaria, Mido Assran, Randall Balestriero, Amir Bar, Adrien Bardes, Yann LeCun, Nicolas Ballas

diffusion reinforcement-learning robotics

arXiv AI

Jun 30

SAGA: Scene-Aware, Goal-Evolving Agents for Long-Horizon CivRealm Strategy Planning

arXiv:2606. 29932v1 Announce Type: new Abstract: Long-horizon strategic planning in complex strategy games demands concurrent reasoning across multiple decision domains under imperfect information and sparse reward.

By Tianyu Jin, Shuo Chen, Yida Wang, Liuyu Xiang, Yingzhuo Liu, Zhiyao Jiang, Yexin Li, Zhaofeng He

llms agents benchmarks

Related stories

SAT-RTS: A systematic framework for tactical knowledge extraction and visualization-based analysis in real-time strategy games

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal

Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution

Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials

Hierarchical Planning with Latent World Models

SAGA: Scene-Aware, Goal-Evolving Agents for Long-Horizon CivRealm Strategy Planning