OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel also includes tools to analyze learning dynamics and other common evaluation metrics. Games are represented as procedural extensive-form games, with some natural extensions.
Open Spiel supports
- Single and multi-player games
- Fully observable (via observations) and imperfect information games (via information states and observations)
- Stochasticity (via explicit chance nodes mostly, even though implicit stochasticity is partially supported)
- n-player normal-form "one-shot" games and (2-player) matrix games
- Sequential and simultaneous move games
- Zero-sum, general-sum, and cooperative (identical payoff) games
Multi-language support
- C++17
- Python 3
- A subset of the features are available in Swift.
The games and utility functions (e.g. exploitability computation) are written in C++. These are also available using pybind11 Python (2.7 and 3) bindings.
The methods names are in CamelCase in C++ and snake_case in Python (e.g.
state.ApplyAction in C++ will be state.apply_action in Python). See the
pybind11 definition in open_spiel/python/pybind11/pyspiel.cc
for the full mapping between names.
For algorithms, many are written in both languages, even if some are only available from Python.
Platforms
OpenSpiel has been tested on Linux (Debian 10 and Ubuntu 19.04), MacOS, and Windows 10 (through Windows Subsystem for Linux).
Visualization of games
There is a basic visualizer based on graphviz, see open_spiel/python/examples/treeviz_example.py.
There is an interactive viewer for OpenSpiel games called SpielViz.