def traffic():
for i in range(100):
observation = env.reset()
t_reward = 0
step = 0
r1 = rnd
r2 = rnd
r1.seed(1)
r2.seed(2)
while True:
step += 1
# time.sleep(0.1)
cars(r1, r2)
env.render()
action = RL.choose_action(observation)
if int(observation[5]) < 6:
# print("can not change")
action = "n"
# print(action)
observation_, reward, done = env.switch_light(action)
t_reward += reward
RL.save_memory(observation, action, reward, observation_)
if step > 500 and step % 5 == 0:
RL.learn()
observation = observation_
if done:
print(t_reward)
break
def main():
for i in range(1, MAX_EPISODES):
print(i, "of episodes", end="\n")
start_time = time.time()
observation = env.reset()
for j in range(MAX_STEP_EPISODES):
env.render()
action = RL.choose_action(observation)
if j < 5:
action = 0
observation_, reward, done, info = env.step(action)
RL.store_transition(observation, action, reward, False)
if done:
RL.store_transition(observation, action, 0.0, True)
RL.learn()
break
observation = observation_
end_time = time.time()
plot_.plot_graph((end_time - start_time), i)
env.close()
RL.store_net()
