StarCraft II, as a real-time strategy game, features multiagent collaboration, complex
decision-making processes, partially observable environments, and long-term credit …

Current MARL benchmarks fall short in simulating realistic scenarios, particularly those
involving long action sequences with sequential tasks and multiple conflicting objectives …

The advent of deep reinforcement learning (DRL) has significantly advanced the field of
robotics, particularly in the control and coordination of quadruped robots. However, the …

This paper introduces a method to explain MADRL agents' behaviors by abstracting their
actions into high-level strategies. Particularly, a spatio-temporal neural network model is …

Cooperative multi-agent reinforcement learning methods aim to learn effective collaborative
behaviours of multiple agents performing complex tasks. However, existing MARL methods …

StarCraft is a popular real-time strategy game that has been widely used as a research
platform for artificial intelligence. Micromanagement refers to the process of making each …

With the development of artificial intelligence technology, especially the promotion of large-
scale pre-training model theory, some new perspectives of strategy solving for intelligent …

This paper introduces a method to explain MADRL agents' behaviors by abstracting their
actions into high-level strategies. Particularly, a spatio-temporal neural network model is …

The field of Multi-Agent Reinforcement Learning (MARL) has rapidly evolved, yet integrating
diverse tasks and algorithms into a cohesive system remains a complex challenge. This …

随着人工智能技术的发展, 特别是大型预训练模型理论的推动, 智能博弈决策策略求解的一些新
视角逐渐受到广泛关注和探讨. 结合人工智能技术的发展与智能博弈决策策略求解范式的转变 …