The API for the halite engine is contained in hlt. Some custom modifications were made to this to make game play and testing easier.
The executable file halite was built on Ubuntu 18.04 so only works there. If you are trying to run this on a Mac or Windows OS then you need to go to https://halite.io/learn-programming-challenge/downloads, download the corresponding python3 starter kit where you will find a prebuilt halite executable. Replace the one in this directory with that and things should work.
This codebase was written in python3.
To run DQN, use:
python -m dqn_learning.train --games <NUM_OF_EPISODES_TO_RUN>To run actor-critic, use:
python -m actor_critic.train --games <NUM_OF_EPISODES_TO_RUN>Note: The codebase is set up to use the simplified reward function, a 10x10 game map and each player
is restricted to one ship. To change the map size, you need to go into the corresponding train.py inside
actor_critic or dq_learning and change this line:
command = "./halite --replay-directory ./replays/ --width 10 --height 10 --no-timeout --results-as-json".split()to this:
command = "./halite --replay-directory ./replays/ --width 32 --height 32 --no-timeout --results-as-json".split()You must also make sure to change the variable map_dim at the top of the same file and set that to 32. This may
require parameter number changes to the models.
Training data is collected using tensorboardX. Refer to https://github.com/lanpa/tensorboardX
for installation procedures. The data will be written to a folder called runs in this directory.
To generate graphs of the data, use the following command in this directory:
tensorboard --logdir runs./clear.shReplays generated during training are put into the replays folder in this directory. The replay files
have the .hlt extension. To view the replay, go to https://halite.io/watch-games and drag and drop the
replay file into the box designated for replays.