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DeepMind details OpenSpiel, a collection of AI training tools for video games

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Reinforcement learning, the AI training technique that’s brought to fruition systems capable of defeating world poker champions and guiding self-driving cars, isn’t the simplest thing in the world to wrangle. That’s particularly true in the gaming domain, where cutting-edge approaches sometimes require bespoke tools that aren’t publicly available.

Fortunately, that’s changing. In a paper recently published on the preprint server Arxiv.org, researchers at Alphabet’s DeepMind describe a game-oriented reinforcement learning framework dubbed OpenSpiel. At its core, it’s a collection of environments and algorithms for research in general reinforcement learning and search and planning in games, with tools to analyze learning dynamics and other common evaluation metrics.

“The purpose of OpenSpiel is to promote general multiagent reinforcement learning across many different game types, in a similar way as general game-playing but with a heavy emphasis on learning and not in competition form,” wrote the researchers. “We hope that OpenSpiel could have a similar effect on general [reinforcement learning] in games as the Atari Learning Environment has had on single-agent [reinforcement learning].”

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Also: FFmpeg Lands OpenCL-Powered Video Stabilization Filter

DeepMind introduces OpenSpiel

  • DeepMind introduces OpenSpiel, a reinforcement learning-based framework for video games

    A few days ago, researchers at DeepMind introduced OpenSpiel, a framework for writing games and algorithms for research in general reinforcement learning and search/planning in games. The core API and games are implemented in C++ and exposed to Python. Algorithms and tools are written both in C++ and Python. It also includes a branch of pure Swift in the swift subdirectory.

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