The Verge’s piece on how Alibaba uses AI to study playing Starcraft. Probably this is the way to attract new brilliant researches into the company, netherless, results look really promising.
http://www.theverge.com/2017/4/3/15164490/alibaba-ai-starcraft-combat
#reinforcementlearning #alibaba #starcraft
http://www.theverge.com/2017/4/3/15164490/alibaba-ai-starcraft-combat
#reinforcementlearning #alibaba #starcraft
The Verge
AI is one step closer to mastering StarCraft
Last year, Alphabet’s DeepMind division captured the world’s attention by besting humanity’s top player in the game of Go. The achievement, which many experts predicted was still a decade off,...
#DeepMind will show AI playing #Starcraft II.
Starts in 8 hours (6:00 PM GMT)
youtube.com/c/deepmind / https://www.twitch.tv/starcraft
#RL
Starts in 8 hours (6:00 PM GMT)
youtube.com/c/deepmind / https://www.twitch.tv/starcraft
#RL
🔥DeepMind’s AlphaStar beats top human players at strategy game StarCraft II
AlphaStar by Google’s DeepMind can now play StarCraft 2 so well that it places in the 99.8 percentile on the European server. In other words, way better than even great human players, achieving performance similar to gods of StarCraft.
Solution basically combines reinforcement learning with a quality-diversity algorithm, which is similar to an evolutionary algorithm.
What’s difficult about StarCraft and how is it different to recent #Go and #Chess AI solutions: even finding winning strategy (StarCraft is famouse to closeness to rock-scissors-paper, not-so-transitive game design, as chess and go), is not enough to win, since the result depends on execution on different macro and micro levels at different timescales.
How that is applicable in real world: basically, it is running logistics, manufacture, research with complex operations and different units.
Why this matters: it brings AI one step closer to running real business.
Blog post: https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning
Nature: https://www.nature.com/articles/d41586-019-03298-6
ArXiV: https://arxiv.org/abs/1902.01724
Nontechnical video: https://www.youtube.com/watch?v=6eiErYh_FeY
#Google #GoogleAI #AlphaStar #Starcraft #Deepmind #nature #AlphaZero
AlphaStar by Google’s DeepMind can now play StarCraft 2 so well that it places in the 99.8 percentile on the European server. In other words, way better than even great human players, achieving performance similar to gods of StarCraft.
Solution basically combines reinforcement learning with a quality-diversity algorithm, which is similar to an evolutionary algorithm.
What’s difficult about StarCraft and how is it different to recent #Go and #Chess AI solutions: even finding winning strategy (StarCraft is famouse to closeness to rock-scissors-paper, not-so-transitive game design, as chess and go), is not enough to win, since the result depends on execution on different macro and micro levels at different timescales.
How that is applicable in real world: basically, it is running logistics, manufacture, research with complex operations and different units.
Why this matters: it brings AI one step closer to running real business.
Blog post: https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning
Nature: https://www.nature.com/articles/d41586-019-03298-6
ArXiV: https://arxiv.org/abs/1902.01724
Nontechnical video: https://www.youtube.com/watch?v=6eiErYh_FeY
#Google #GoogleAI #AlphaStar #Starcraft #Deepmind #nature #AlphaZero
YouTube
The AI that mastered Starcraft II
Google’s DeepMind artificial intelligence researchers have already mastered games like Pong, Chess and Go but their latest triumph is on another planet. AlphaStar is an artificial intelligence trained to play the science fiction video game StarCraft II.
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QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning
Paper extends the Deep Quality-Value (DQV) family of al-
gorithms to multi-agent reinforcement learning and outperforms #SOTA
ArXiV: https://arxiv.org/abs/2012.12062
#DQV #RL #Starcraft
Paper extends the Deep Quality-Value (DQV) family of al-
gorithms to multi-agent reinforcement learning and outperforms #SOTA
ArXiV: https://arxiv.org/abs/2012.12062
#DQV #RL #Starcraft