This project is an attempt to use evolutionary algorithms and coevolution to learn an effective strategy for playing Halite. The problem of playing the game is decomposed into two strategies; strategy for controlling the ships and strategy for controlling the shipyards. With that decomposition the problem is transformed into multi-agent optimization problem on which we can apply cooperative coevolution. Strategies for controlling ships and shipyards are evolved in separate populations (hence coevolution). It uses different evolutionary algorithms (GA/ES) and models (NN/CGP/GP) for the evolution of both the strategies.
Install the required packages
pip install -r requirements.txt
To run the coevolution, you need to provide a configuration file in TOML format.
An example configuration is provided in example.toml.
You can run the coevolution with the following command:
python main.py example.toml
After running the coevolution, the results are stored in the current directory in the following files:
ship-population.pkl: Last population of the ship strategies (pickled)yard-population.pkl: Last population of the shipyard strategies (pickled)ship-hof.pkl: Hall of fame of the ship strategies (pickled)yard-hof.pkl: Hall of fame of the shipyard strategies (pickled)ship-logbook.pkl: Logbook of the ship subevolution (pickled)yard-logbook.pkl: Logbook of the shipyard subevolution (pickled)coevolution.png: Plot of the average fitness of the both subevolutions over generationsship.png: Plot of the average, median, best and worst fitness of the ship subevolution over generationsyard.png: Plot of the average, median, best and worst fitness of the shipyard subevolution over generationsbest_fit.txt: The best fitness values of the ship and shipyard subevolutions in the whole run
(It is recommended to run the coevolution in a separate directory, as it will create a lot of files.)