def __init__(self, config, db, neural, weboutput=[]):
self._config = config
self._database = db
self._neural = neural
self._weboutput = weboutput
self._batchSize = int(self._config["batchSize"])
self._windowSize = int(self._config["windowSize"])
self._numCoins = len(self._config["coins"])
self._learningRate = self._config["learningRate"]
self._decayRate = self._config["decayRate"]
self._decaySteps = self._config["decaySteps"]
self._trainTestSplit = self._config["trainTestSplit"]
self._interval = self._config["tradeInterval"]
self._startutc, self._endutc = self._database.rangeUtcstamp()
self._startutc = max(self._config["startUtc"], self._startutc)
self._allX = self._database.readAll(self._startutc, self._endutc, False)
self._replayMemory = replay.ReplayMemory(config,fromutc=self._startutc,toutc=self._endutc)
self._commission = self._config["commission"]
self._startTrainUtc = self._startutc
self._endTrainUtc = self._startutc + int(self._trainTestSplit * (self._endutc - self._startutc))
self._endTrainUtc = self._endTrainUtc - (self._endTrainUtc % self._interval)
self._startTestUtc = self._endTrainUtc
self._endTestUtc = self._endutc
self.initTensors()
self._saver = tf.train.Saver(max_to_keep=5)
help="tensorflow model checkpoint file to initialize from")
parser.add_argument("rom", help="rom file to run")
args = parser.parse_args()
print 'Arguments: %s' % (args)
baseOutputDir = 'game-out-' + time.strftime("%Y-%m-%d-%H-%M-%S")
os.makedirs(baseOutputDir)
State.setup(args)
environment = AtariEnvironment(args, baseOutputDir)
dqn = dqn.DeepQNetwork(environment.getNumActions(), baseOutputDir, args)
replayMemory = replay.ReplayMemory(args)
def runEpoch(minEpochSteps, evalWithEpsilon=None):
stepStart = environment.getStepNumber()
isTraining = True if evalWithEpsilon is None else False
startGameNumber = environment.getGameNumber()
epochTotalScore = 0
while environment.getStepNumber() - stepStart < minEpochSteps:
startTime = lastLogTime = time.time()
stateReward = 0
state = None
while not environment.isGameOver():
# setup
#################################
base_output_dir = 'run-out-' + time.strftime("%Y-%m-%d-%H-%M-%S")
os.makedirs(base_output_dir)
tensorboard_dir = base_output_dir + "/tensorboard/"
os.makedirs(tensorboard_dir)
summary_writer = tf.summary.create_file_writer(tensorboard_dir)
with summary_writer.as_default():
tf.summary.text('params', str(args), step=0)
State.setup(args)
environment = CarEnv(args)
replay_memory = replay.ReplayMemory(base_output_dir, args)
dqn = dqn.DeepQNetwork(environment.get_num_actions(),
environment.get_state_size(), replay_memory,
base_output_dir, tensorboard_dir, args)
train_epsilon = args.epsilon #don't want to reset epsilon between epoch
start_time = datetime.datetime.now()
train_episodes = 0
eval_episodes = 0
episode_train_reward_list = []
episode_eval_reward_list = []
#################################
# stop handler
#################################
def __init__(self, model, optimizer, criterion):
self.model = model
self.optimizer = optimizer
self.criterion = criterion
self.memory = replay.ReplayMemory()
