MineRL sample-efficient reinforcement learning challenge—back for a second year—benefits organizers, as well as larger research community

To unearth a diamond in the block-based open world of Minecraft requires the acquisition of materials and the construction of tools before any diamond mining can even begin. Players need to gather wood, which they’ll use to make a wood pickaxe for mining stone underground. They’ll use the stone to fashion a stone pickaxe and, with the tool upgrade, mine iron ore. They’ll build a furnace for smelting the iron and use that to make the iron pickaxe they need […]

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Adversarial robustness as a prior for better transfer learning

Editor’s note: This post and its research are the collaborative efforts of our team, which includes Andrew Ilyas (PhD Student, MIT), Logan Engstrom (PhD Student, MIT), Aleksander Mądry (Professor at MIT), Ashish Kapoor (Partner Research Manager). In practical machine learning, it is desirable to be able to transfer learned knowledge from some “source” task to downstream “target” tasks. This is known as transfer learning—a simple and efficient way to obtain performant machine learning models, especially when there is little training […]

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Three new reinforcement learning methods aim to improve AI in gaming and beyond

Reinforcement learning (RL) provides exciting opportunities for game development, as highlighted in our recently announced Project Paidia—a research collaboration between our Game Intelligence group at Microsoft Research Cambridge and game developer Ninja Theory. In Project Paidia, we push the state of the art in reinforcement learning to enable new game experiences. In particular, we focus on developing game agents that learn to genuinely collaborate in teams with human players. In this blog post we showcase three of our recent research […]

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