def load(self, path):
"""
"""
ReplayMemory.load(self, path)
self.priority = None
f = open(path + '/priority.npy')
self.priority = np.load(f)
f.close()
tensorboard_monitor = TensorboardMonitor('./log', sess, counter)
tensorboard_monitor.add_scalar_summary('score', 'per_game_summary')
tensorboard_monitor.add_scalar_summary('training_loss', 'training_summary')
for i in range(4):
tensorboard_monitor.add_histogram_summary('Q%d_training' % i, 'training_summary')
checkpoint_monitor = CheckpointRecorder(dqn_agent.dqn, replay_memory, counter, './checkpoints', sess)
agi.add_listener(checkpoint_monitor)
agi.add_listener(tensorboard_monitor)
dqn_agent.add_listener(tensorboard_monitor)
sess.run(tf.global_variables_initializer())
# Load the DQN and replay memory
dqn_agent.dqn.restore('./checkpoints/dqn/7000000')
replay_memory.load('./checkpoints/replay_memory/7000000')
dqn_agent.update_target_network()
def run():
cur_episode = 0
num_frames = 7000000
while counter.count < 50000000:
score = agi.learn()
tensorboard_monitor.record({'score': score})
elapsed_frames = counter.count - num_frames
num_frames = counter.count
print "Episode %d: Total Score = %d\t# Frames = %d\tTotal Frames = %d\tEpsilon: %f" % (cur_episode, score, elapsed_frames, num_frames, agent.epsilon)
cur_episode += 1