We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies, each represented by deep neural networks5,6. Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players.
Over the course of a decade and numerous competitions1,2,3, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems⁴. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments.
As an outcome, we provide an overview a body of work that lies at the intersection of three disciplines: Artificial intelligence, robotics, and games.
We highlight significant trends in the research conducted in the field and to provide commentaries and insights, about challenges and achievements in generating decision-making processes for multirobot adversarial scenarios. In this survey, we dive into the techniques developed within theįramework by analyzing and commenting on them in detail. Robotic soccer teams are complex multirobot systems, where each unit shows individual skills, and solid teamwork by exchanging information about their local perceptions and intentions. Has a prominent role, running one of the first worldwide multirobot competition (in the late 1990s), challenging researchers to develop robotic systems able to compete in the game of soccer. The research community in artificial intelligence and robotics has launched robotic competitions to promote research and validate new approaches, by providing robust benchmarks to evaluate all the components of a multiagent system-ranging from hardware to high-level strategy learning. Effective team strategies and joint decision-making processes are fundamental in modern robotic applications, where multiple units have to cooperate to achieve a common goal.
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