Game AI: Your starting point for AI research

NOTE: This is still a work in progress and the API might change significantly before a stable release. Use at your own risk.

Welcome to gameai! This package contains a series of well-defined abstractions that are common in AI, such as a games, agents, and trainers which optimize the behavior of agents. As long as a class inherits from the base implemetation of a primitive (e.g. Agent) and implements the required methods, it can be used in place of the standard implementations given. To get started:

from gameai.games import TicTacToe
from gameai.agents import RandomAgent, MCTSAgent
from gameai.core import Arena, Player
# Create our game
game = TicTacToe()
# Inititalize our agents
mcts_agent = MCTSAgent()
random_agent = RandomAgent()
# We train the mcts agent
mcts_agent.train(game, verbose=True, num_iters=10000, num_episodes=100)
player0 = Player(0, mcts_agent)
player1 = Player(1, random_agent)
# Pit the agents against eachother in the arena. Note that the player
# ids passed in need to match the index of the player in the array
arena = Arena(game, [player0, player1])
arena.play_games(verbose=True)
arena.statistics()

Installation

Because this is still in alpha and under active development, it has not been released to PyPi. You can install via TestPyPi using the following command:

pip install --index-url https://test.pypi.org/simple/ gameai

Running locally

Clone the repository and install the neccessary dependencies with pip install -r requirements.txt. You will also need to setup a virtual environment and run pip install -e . in the root of the project, which will install the package locally.

Testing

Testing is done with pytest. Run the command make test to run all tests.

Building

Build the project with python3 setup.py sdist bdist_wheel.

Roadmap

Add Monte Carlo Tree Search
Add Alpha Beta Pruning
Add Othello as a game
Add Chess as a game
Add a simple RL agent & trainer
Add an alphazero agent & trainer

Contribution

Feel free to open an issue / submit a PR!