def learn_game(game_path: str, num_iterations: int, refresh: bool):
game = parser.parse_xml(game_path)
game.start([randomplayer.RandomPlayer(0), randomplayer.RandomPlayer(1)])
input_mapper_path = game.name + INPUT_MAPPER_FILE_SUFFIX
input_mapper = NeuralNetworkInput(game) if refresh or not os.path.exists(input_mapper_path) \
else read_mapper(input_mapper_path)
output_mapper_path = game.name + OUTPUT_MAPPER_FILE_SUFFIX
output_mapper = NeuralNetworkOutputMapper(game) if refresh or not os.path.exists(output_mapper_path) \
else read_mapper(output_mapper_path)
while output_mapper.missing_mappings():
players = [ExploratoryPlayer(0, output_mapper), ExploratoryPlayer(1, output_mapper)]
parser.parse_xml(game_path).start(players)
chooser_path = game.name + CHOICE_FILE_SUFFIX
chooser = create_neural_network(input_mapper.input_length, output_mapper.output_length) if refresh \
else load_or_create_neural_network(chooser_path, input_mapper.input_length, output_mapper.output_length)
scorer_path = game.name + SCORER_FILE_SUFFIX
scorer = create_neural_network(input_mapper.input_length, 1) if refresh \
else load_or_create_neural_network(scorer_path, input_mapper.input_length, 1)
players = [
LearningPlayer(0, input_mapper, output_mapper, scorer, chooser),
manualplayer.ManualPlayer(1)
]
wins = 0
losses = 0
def save():
save_mapper(input_mapper_path, input_mapper)
save_mapper(output_mapper_path, output_mapper)
chooser.save(chooser_path)
scorer.save(scorer_path)
for i in range(int(num_iterations)):
print("Game "+str(i))
winners = parser.parse_xml(game_path).start(players)
wins += int(all(isinstance(winner, LearningPlayer) for winner in winners))
losses += int(not(any(isinstance(winner, LearningPlayer) for winner in winners)))
if i % 100 == 0:
save()
print("Winners:"+str(wins))
print("Losers:"+str(losses))
save()
def run_game(game_path, players):
game = parser.parse_xml(game_path)
game.start(players)
return game
