This Wednesday, Jonathan presented the paper “Grandmaster level in StarCraft II using multi-agent reinforcement learning”. The abstract of the paper is presented below.

In case you missed this session, you can check out their paper here. Follow our Facebook event for our up-to-date lineup of papers for this term, and like our Facebook page to keep informed of the other events that we’ll be doing!

CUMIN’s paper reading group happens weekly on Wednesdays 2pm in the North Room, Department of Engineering. No prior reading of the paper is required, just show up to find out more. All are welcome!

Check out our paper reading page for information on other regular paper reading groups that are going on around Cambridge.


Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. 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. Over the course of a decade and numerous competitions, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems. Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players. We chose to address the challenge of StarCraft using generalpurpose 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 networks. We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players


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