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A3C_distributed_tf.py
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A3C_distributed_tf.py
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"""
Asynchronous Advantage Actor Critic (A3C) with discrete action space, Reinforcement Learning.
The Cartpole example using distributed tensorflow + multiprocessing.
View more on my tutorial page: https://morvanzhou.github.io/
"""
import multiprocessing as mp
import tensorflow as tf
import numpy as np
import gym, time
import matplotlib.pyplot as plt
UPDATE_GLOBAL_ITER = 10
GAMMA = 0.9
ENTROPY_BETA = 0.001
LR_A = 0.001 # learning rate for actor
LR_C = 0.001 # learning rate for critic
env = gym.make('CartPole-v0')
N_S = env.observation_space.shape[0]
N_A = env.action_space.n
class ACNet(object):
sess = None
def __init__(self, scope, opt_a=None, opt_c=None, global_net=None):
if scope == 'global_net': # get global network
with tf.variable_scope(scope):
self.s = tf.placeholder(tf.float32, [None, N_S], 'S')
self.a_params, self.c_params = self._build_net(scope)[-2:]
else:
with tf.variable_scope(scope):
self.s = tf.placeholder(tf.float32, [None, N_S], 'S')
self.a_his = tf.placeholder(tf.int32, [None, ], 'A')
self.v_target = tf.placeholder(tf.float32, [None, 1], 'Vtarget')
self.a_prob, self.v, self.a_params, self.c_params = self._build_net(scope)
td = tf.subtract(self.v_target, self.v, name='TD_error')
with tf.name_scope('c_loss'):
self.c_loss = tf.reduce_mean(tf.square(td))
with tf.name_scope('a_loss'):
log_prob = tf.reduce_sum(
tf.log(self.a_prob) * tf.one_hot(self.a_his, N_A, dtype=tf.float32),
axis=1, keep_dims=True)
exp_v = log_prob * tf.stop_gradient(td)
entropy = -tf.reduce_sum(self.a_prob * tf.log(self.a_prob + 1e-5),
axis=1, keep_dims=True) # encourage exploration
self.exp_v = ENTROPY_BETA * entropy + exp_v
self.a_loss = tf.reduce_mean(-self.exp_v)
with tf.name_scope('local_grad'):
self.a_grads = tf.gradients(self.a_loss, self.a_params)
self.c_grads = tf.gradients(self.c_loss, self.c_params)
self.global_step = tf.train.get_or_create_global_step()
with tf.name_scope('sync'):
with tf.name_scope('pull'):
self.pull_a_params_op = [l_p.assign(g_p) for l_p, g_p in zip(self.a_params, global_net.a_params)]
self.pull_c_params_op = [l_p.assign(g_p) for l_p, g_p in zip(self.c_params, global_net.c_params)]
with tf.name_scope('push'):
self.update_a_op = opt_a.apply_gradients(zip(self.a_grads, global_net.a_params), global_step=self.global_step)
self.update_c_op = opt_c.apply_gradients(zip(self.c_grads, global_net.c_params))
def _build_net(self, scope):
w_init = tf.random_normal_initializer(0., .1)
with tf.variable_scope('actor'):
l_a = tf.layers.dense(self.s, 200, tf.nn.relu6, kernel_initializer=w_init, name='la')
a_prob = tf.layers.dense(l_a, N_A, tf.nn.softmax, kernel_initializer=w_init, name='ap')
with tf.variable_scope('critic'):
l_c = tf.layers.dense(self.s, 100, tf.nn.relu6, kernel_initializer=w_init, name='lc')
v = tf.layers.dense(l_c, 1, kernel_initializer=w_init, name='v') # state value
a_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope + '/actor')
c_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope + '/critic')
return a_prob, v, a_params, c_params
def choose_action(self, s): # run by a local
prob_weights = self.sess.run(self.a_prob, feed_dict={self.s: s[np.newaxis, :]})
action = np.random.choice(range(prob_weights.shape[1]),
p=prob_weights.ravel()) # select action w.r.t the actions prob
return action
def update_global(self, feed_dict): # run by a local
self.sess.run([self.update_a_op, self.update_c_op], feed_dict) # local grads applies to global net
def pull_global(self): # run by a local
self.sess.run([self.pull_a_params_op, self.pull_c_params_op])
def work(job_name, task_index, global_ep, lock, r_queue, global_running_r):
# set work's ip:port
cluster = tf.train.ClusterSpec({
"ps": ['localhost:2220', 'localhost:2221',],
"worker": ['localhost:2222', 'localhost:2223', 'localhost:2224', 'localhost:2225',]
})
server = tf.train.Server(cluster, job_name=job_name, task_index=task_index)
if job_name == 'ps':
print('Start Parameter Sever: ', task_index)
server.join()
else:
t1 = time.time()
env = gym.make('CartPole-v0').unwrapped
print('Start Worker: ', task_index)
with tf.device(tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % task_index,
cluster=cluster)):
opt_a = tf.train.RMSPropOptimizer(LR_A, name='opt_a')
opt_c = tf.train.RMSPropOptimizer(LR_C, name='opt_c')
global_net = ACNet('global_net')
local_net = ACNet('local_ac%d' % task_index, opt_a, opt_c, global_net)
# set training steps
hooks = [tf.train.StopAtStepHook(last_step=100000)]
with tf.train.MonitoredTrainingSession(master=server.target,
is_chief=True,
hooks=hooks,) as sess:
print('Start Worker Session: ', task_index)
local_net.sess = sess
total_step = 1
buffer_s, buffer_a, buffer_r = [], [], []
while (not sess.should_stop()) and (global_ep.value < 1000):
s = env.reset()
ep_r = 0
while True:
# if task_index:
# env.render()
a = local_net.choose_action(s)
s_, r, done, info = env.step(a)
if done: r = -5.
ep_r += r
buffer_s.append(s)
buffer_a.append(a)
buffer_r.append(r)
if total_step % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net
if done:
v_s_ = 0 # terminal
else:
v_s_ = sess.run(local_net.v, {local_net.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.array(buffer_a), np.vstack(
buffer_v_target)
feed_dict = {
local_net.s: buffer_s,
local_net.a_his: buffer_a,
local_net.v_target: buffer_v_target,
}
local_net.update_global(feed_dict)
buffer_s, buffer_a, buffer_r = [], [], []
local_net.pull_global()
s = s_
total_step += 1
if done:
if r_queue.empty(): # record running episode reward
global_running_r.value = ep_r
else:
global_running_r.value = .99 * global_running_r.value + 0.01 * ep_r
r_queue.put(global_running_r.value)
print(
"Task: %i" % task_index,
"| Ep: %i" % global_ep.value,
"| Ep_r: %i" % global_running_r.value,
"| Global_step: %i" % sess.run(local_net.global_step),
)
with lock:
global_ep.value += 1
break
print('Worker Done: ', task_index, time.time()-t1)
if __name__ == "__main__":
# use multiprocessing to create a local cluster with 2 parameter servers and 2 workers
global_ep = mp.Value('i', 0)
lock = mp.Lock()
r_queue = mp.Queue()
global_running_r = mp.Value('d', 0)
jobs = [
('ps', 0), ('ps', 1),
('worker', 0), ('worker', 1), ('worker', 2), ('worker', 3)
]
ps = [mp.Process(target=work, args=(j, i, global_ep, lock, r_queue, global_running_r), ) for j, i in jobs]
[p.start() for p in ps]
[p.join() for p in ps[2:]]
ep_r = []
while not r_queue.empty():
ep_r.append(r_queue.get())
plt.plot(np.arange(len(ep_r)), ep_r)
plt.title('Distributed training')
plt.xlabel('Step')
plt.ylabel('Total moving reward')
plt.show()