"episode: {}/{}, score: {}, e's: {}\nNumber of Controller breaks : {}"
.format(episode, EPISODES, running_meta_reward, epsilon,
no_controller_breaks))
print("The state is : ", meta_end_state)
break
confidence_state = next_confidence_state
if episode % 200 == 0:
print("Episodes : {}".format(episode))
# Saving the progress
print("Saving")
# convert this to save model for each policy
MetaAgent.save(filename)
# agent.saveController(fileController)
sleep(0.2)
print("Done Saving You can Now Quit")
sleep(1)
if __name__ == "__main__":
if args['set_gpu'] is not None:
print("Using the GPU ")
DQNAgent.setup_gpu(int(args['set_gpu']))
else:
print("{LOG} Using the CPU for Computation")
pass
main()
"episode: {}/{}, score: {}, e's: {}\nNumber of Controller breaks : {}"
.format(episode, EPISODES, running_reward, epsilon,
no_controller_breaks))
print("The state is : ", meta_end_state)
break
confidence_state = next_confidence_state
if episode % 200 == 0:
print("Episodes : {}".format(episode))
# Saving the progress
print("Saving")
# convert this to save model for each policy
MetaAgent.save(filename)
# agent.saveController(fileController)
sleep(0.2)
print("Done Saving You can Now Quit")
sleep(1)
if __name__ == "__main__":
if args['set_gpu'] is not None:
print("Using the GPU ")
DQNAgent.setup_gpu(str(args['set_gpu']))
else:
print("{LOG} Using the CPU for Computation")
pass
main()