def make_bot(un, pw, expected_opponent, team, challenge, trainer, epsilon=None, model_path=None, target_model_path=None ): if trainer: if model_path: agent = DQNAgent(INPUT_SHAPE, training=False) else: agent = RandomAgent() else: agent = DQNAgent( INPUT_SHAPE, epsilon=epsilon, random_moves=True, training=False, copy_target_model=False ) agent.load_model(model_path) if target_model_path != None: agent.target_model = load_model(target_model_path) else: agent.target_model.set_weights(agent.model.get_weights()) bot = BotClient( name=un, password=pw, expected_opponent=expected_opponent, team=team, challenge=challenge, runType=RunType.Iterations, runTypeData=1, agent=agent, trainer=trainer, save_model=False, should_write_replay=(not trainer) ) bot.start()
for i in range(5):
try:
os.remove(file_path)
break
except PermissionError:
debug_log('Permission error when removing the file')
time.sleep(1)
#NOTE: train
#NOTE: create/load DQN and target DQN in main thread
keras.backend.clear_session()
agent = DQNAgent(INPUT_SHAPE, training=True,
replay_memory=minibatch, copy_target_model=False
)
agent.target_model = load_model(target_model_path)
#NOTE: train newly loaded model on new data
if len(minibatch) > 0:
minibatch_history = agent.train_only(len(minibatch), len(minibatch))
if minibatch_history == None:
debug_log('ERROR: Unable to train on iteration\'s data')
replay_memory.extend(minibatch)
else:
debug_log('WARNING: Skipping minibatch training since no new data was found')
#NOTE: train newly loaded model on random selection of old data
agent.replay_memory = replay_memory
sum_loss = 0
if len(replay_memory) > MIN_REPLAY_MEMORY_SIZE:
train_loops = 50
for train_iteration in range(train_loops):
