class MobileAvoidance(EnvSpace):
def env_init(self):
self.env = CarEnv()
self.state = self.env.reset()
self.send_state_get_action(self.state)
self.var = 1
def on_predict_response(self, action):
self.var = self.var * 0.9995 if self.ep_use_step > cfg['DDPG'][
'memory_capacity'] else self.var
a = np.clip(np.random.normal(action, self.var), *self.env.action_bound)
next_state, reward, done, _ = self.env.step(action)
# print(next_state)
done = True if self.ep_use_step >= EP_MAXSTEP else done
self.send_train_get_action(self.state, action, reward, done,
next_state)
self.state = next_state
# print('self.env_name=',self.env_name)
if self.ep >= 30 and RENDER:
self.env.render()
if done:
self.state = self.env.reset()
self.send_state_get_action(self.state)
class Worker(object):
def __init__(self, wid):
self.wid = wid
self.env = CarEnv()
self.ppo = GLOBAL_PPO
def work(self):
global GLOBAL_EP, GLOBAL_RUNNING_R, GLOBAL_UPDATE_COUNTER
while not COORD.should_stop():
s = self.env.reset()
ep_r = 0
buffer_s, buffer_a, buffer_r = [], [], []
for t in range(EP_LEN):
if not ROLLING_EVENT.is_set(): # while global PPO is updating
ROLLING_EVENT.wait() # wait until PPO is updated
buffer_s, buffer_a, buffer_r = [], [], [
] # clear history buffer
a = self.ppo.choose_action(s)
s_, r, done = self.env.step(a)
buffer_s.append(s)
buffer_a.append(a)
buffer_r.append(r) # normalize reward, find to be useful
s = s_
ep_r += r
GLOBAL_UPDATE_COUNTER += 1 # count to minimum batch size
if t == EP_LEN - 1 or GLOBAL_UPDATE_COUNTER >= MIN_BATCH_SIZE or done == 1:
v_s_ = self.ppo.get_v(s_)
discounted_r = [] # compute discounted reward
for r in buffer_r[::-1]:
v_s_ = r + GAMMA * v_s_
discounted_r.append(v_s_)
discounted_r.reverse()
bs, ba, br = np.vstack(buffer_s), np.vstack(
buffer_a), np.array(discounted_r)[:, np.newaxis]
buffer_s, buffer_a, buffer_r = [], [], []
QUEUE.put(np.hstack((bs, ba, br)))
if GLOBAL_UPDATE_COUNTER >= MIN_BATCH_SIZE:
ROLLING_EVENT.clear() # stop collecting data
UPDATE_EVENT.set() # globalPPO update
if GLOBAL_EP >= EP_MAX: # stop training
COORD.request_stop()
break
if t == EP_LEN - 1 or done == 1:
break
# record reward changes, plot later
if len(GLOBAL_RUNNING_R) == 0: GLOBAL_RUNNING_R.append(ep_r)
else:
GLOBAL_RUNNING_R.append(GLOBAL_RUNNING_R[-1] * 0.9 +
ep_r * 0.1)
GLOBAL_EP += 1
print(
'{0:.1f}%'.format(GLOBAL_EP / EP_MAX * 100),
'|W%i' % self.wid,
'|Ep_r: %.2f' % ep_r,
)
class Worker(object):
def __init__(self, name, globalAC):
self.env = CarEnv()
self.name = name
self.AC = ACNet(name, globalAC)
def work(self):
global GLOBAL_RUNNING_R, GLOBAL_EP
total_step = 1
buffer_s, buffer_a, buffer_r = [], [], []
while not COORD.should_stop() and GLOBAL_EP < MAX_GLOBAL_EP:
s = self.env.reset()
ep_r = 0
for ep_t in range(MAX_EP_STEP):
# if self.name == 'W_0':
# self.env.render()
a = self.AC.choose_action(s)
s_, r, done = self.env.step(a)
if ep_t == MAX_EP_STEP - 1: done = True
ep_r += r
buffer_s.append(s)
buffer_a.append(a)
buffer_r.append(r)
if total_step % UPDATE_GLOBAL_ITER == 0 or done or ep_t == MAX_EP_STEP - 1: # update global and assign to local net
if done:
v_s_ = 0 # terminal
else:
v_s_ = SESS.run(self.AC.v,
{self.AC.s: s_[np.newaxis, :]})[0, 0]
buffer_v_target = []
for r in buffer_r[::-1]: # reverse buffer r
v_s_ = r + GAMMA * v_s_
buffer_v_target.append(v_s_)
buffer_v_target.reverse()
buffer_s, buffer_a, buffer_v_target = np.vstack(
buffer_s), np.vstack(buffer_a), np.vstack(
buffer_v_target)
feed_dict = {
self.AC.s: buffer_s,
self.AC.a_his: buffer_a,
self.AC.v_target: buffer_v_target,
}
test = self.AC.update_global(feed_dict)
buffer_s, buffer_a, buffer_r = [], [], []
self.AC.pull_global()
s = s_
total_step += 1
if done or ep_t == MAX_EP_STEP - 1:
if len(GLOBAL_RUNNING_R
) == 0: # record running episode reward
GLOBAL_RUNNING_R.append(ep_r)
else:
GLOBAL_RUNNING_R.append(0.9 * GLOBAL_RUNNING_R[-1] +
0.1 * ep_r)
print(
self.name,
"Ep:",
GLOBAL_EP,
"| Ep_r: %i" % GLOBAL_RUNNING_R[-1],
'| Var:',
test,
)
GLOBAL_EP += 1
break
UPDATE_EVENT.clear() # no update now
ROLLING_EVENT.set() # start to roll out
workers = [Worker(wid=i) for i in range(N_WORKER)]
GLOBAL_UPDATE_COUNTER, GLOBAL_EP = 0, 0
GLOBAL_RUNNING_R = []
COORD = tf.train.Coordinator()
QUEUE = queue.Queue()
threads = []
for worker in workers: # worker threads
t = threading.Thread(target=worker.work, args=())
t.start()
threads.append(t)
# add a PPO updating thread
threads.append(threading.Thread(target=GLOBAL_PPO.update, ))
threads[-1].start()
COORD.join(threads)
# plot reward change and testing
plt.plot(np.arange(len(GLOBAL_RUNNING_R)), GLOBAL_RUNNING_R)
plt.xlabel('Episode')
plt.ylabel('Moving reward')
plt.ion()
plt.show()
while True:
s = env.reset()
for t in range(400):
env.render()
s, _, done = env.step(GLOBAL_PPO.choose_action(s))[0]
if done == 1:
break
from car_env import CarEnv
import pygame
env = CarEnv()
state = env.reset()
close_screen = False
while True:
action = 4
for event in pygame.event.get():
if event.type == pygame.KEYDOWN and event.key == pygame.K_DOWN: action = 0
if event.type == pygame.KEYDOWN and event.key == pygame.K_RIGHT: action = 1
if event.type == pygame.KEYDOWN and event.key == pygame.K_UP: action = 2
if event.type == pygame.KEYDOWN and event.key == pygame.K_LEFT: action = 3
if event.type == pygame.QUIT:
close_screen = True
next_state, reward, done, info = env.step(action)
env.render()
if done or close_screen:
break
pygame.display.quit()
pygame.quit()
