# os.mkdir(episodeDirectory)
tetris.start()
state = tetris.get_state()
episodeReward = 0
step = 0
while step < maxStepPerEpisode:
# if render and numSteps % renderStepDuration == 0:
# image = tetris.get_printed_state()
# plt.imshow(image)
# plt.savefig(episodeDirectory + "s%d.jpg" % step)
action = select_action(state)
step += 1
next_state, reward, done = tetris.step(action)
if done:
next_state = None
memory.add((state, action, reward, next_state))
state = next_state
episodeReward += reward
train()
numSteps += 1
if numSteps % numStepPerUpdate == 0:
targetNet.load_state_dict(policyNet.state_dict())
if done:
break
from tetris.tetris import Tetris
T = Tetris(render=True)
T.start()
T.print_state()
while True:
action = int(input("Action: "))
if action < 0:
T.start()
else:
T.step(action)
T.print_state()