This project aims to learn to play the game 'Hare and Hounds' using temporal difference learning. It consists of multiple programs, one for training the system, and one for running the game with the trained parameters

This program reads in a configuration file and train the system using the parameters given in the configuration. It also requires a number of workers it can use to train multiple configurations at the same time. Trained Q values get stored in a file in the directory trainedQs, where the filename is the parameters that were used.

The configuration is a json file which should have the fields runs, gamma, eta and width, where runs is a single integer and the other ones are arrays. The values are combined in each possible way and this is the eventual configuration used.

This program reads in all the .q files in the directory trainedQs and plays a given number of games with each of the configurations. This number of games has to be given at the command line. It will then for each configuration give the number of wins of the Hounds and the Hares and the parameters given during training.