batch_size = args.BatchSize
Model_Name = "Acrobot-dqn.h5"
agent = AI(action_size, input_shape, batch_size)
if "Acrobot-dqn.h5" in os.listdir():
agent.load(Model_Name)
Epochs = args.Epochs
temp = []
for e in range(Epochs):
state = env.reset()
state = np.reshape(state, [1,input_shape[0],input_shape[1]])
logger.info("Creating Observation ")
for state_count in range(1,1000):
env.render()
logger.info("Sate no {}".format(state_count))
action = agent.act(state)
next_state, reward, done, info = env.step(action)
next_state = np.reshape(next_state, [1,input_shape[0],input_shape[1]])
agent.remember(state, action, reward, next_state, done)
state = next_state
if done:
break
if state_count % batch_size == 0:
agent.replay()
if state_count % 100 == 0:
logging.info("Saving Model")
agent.save(Model_Name)
a = np.amax(agent.getModel().predict(state))
