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

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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.

In their paper, the researchers write, “We hope that OpenSpiel could have a similar effect on general RL in games as the Atari Learning Environment has had on single-agent RL.”

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OpenSpiel allows evaluating written games and algorithms on a variety of benchmark games as it includes implementations of over 20 different games types including simultaneous move, perfect and imperfect information games, gridworld games, an auction game, and several normal-form / matrix games, etc. It includes tools to analyze learning dynamics and other common evaluation metrics. It also supports n-player (single- and multi-agent) zero-sum, cooperative and general-sum, one-shot and sequential games, etc.

OpenSpiel has been tested on Linux (Debian 10 and Ubuntu 19.04). However, the researchers have not tested the framework on MacOS or Windows. “since the code uses freely available tools, we do not anticipate any (major) problems compiling and running under other major platforms,” the researchers added.

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,”  the researcher paper mentions. This framework is “designed to be easy to install and use, easy to understand, easy to extend (“hackable”), and general/broad.”

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Design constraints for OpenSpiel

The two main design criteria that OpenSpiel is based on include:

Simplicity: OpenSpiel provides easy-to-read, easy-to-use code that can be used to learn from and to build a prototype rather than a fully-optimized code that would require additional assumptions.

Dependency-free: Researchers say, “dependencies can be problematic for long-term compatibility, maintenance, and ease-of-use.” Hence, the OpenSpiel framework does not introduce dependencies thus keeping it portable and easy to install.

Swift OpenSpiel: A port to use Swift for TensorFlow

The swift/ folder contains a port of OpenSpiel to use Swift for TensorFlow. This Swift port explores using a single programming language for the entire OpenSpiel environment, from game implementations to the algorithms and deep learning models.

This Swift port is intended for serious research use. As the Swift for TensorFlow platform matures and gains additional capabilities (e.g. distributed training), expect the kinds of algorithms that are expressible and tractable to train to grow significantly.

While OpenSpiel has some tools for visualization and evaluation, the α-Rank algorithm is also a tool. The α-Rank algorithm leverages evolutionary game theory to rank AI agents interacting in multiplayer games. OpenSpiel currently supports using α-Rank for both single-population (symmetric) and multi-population games.

Developers are excited about this release and want to try out this framework.

To know more about this news in detail, head over to the research paper. You can also check out the GitHub page.

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