#load
agent.load("./save/example_dqn.h5")
done = False
batch_size = 32
for e in range(EPISODES):
state = env.reset()
state = np.reshape(state, [1, state_size])
# print(e)
last_reward = 0
for time in range(1000):
# delay.sleep(1/50)
#render
env.render()
# action = agent.act(state)
action = agent.act_2(state)
commands = actions[action]
# if env.key != '':
# # print(env.key-297)
# commands = actions[env.key-257]
# else:
# commands = actions[0]
# print(commands)
# print(state)
next_state, reward, done, _ = env.step2(commands)
# reward = reward if not done else -10
if (time == 0):
last_reward = reward - 1