Wrapper around a simulation of SPL-T, to fit it into the gym reinforcement learning framework.
git clone https://github.com/KPLauritzen/gym-splt
cd gym-splt
pip install -e .
To initialize the env:
import gym
import gym_splt
env = gym.make('splt-v0')
See gym docs for further instruction in how to interact with a gym.Env.
Inspired by the game SPL-T by SIMOGO.
The core simulation was done by Craig Polley in brute_spl-t. Read his excellent write-up on taking lots of random actions in the game here with HN discussion here.