add a3c code

Author: pinard.liu

reinforcement-learning/a3c.py

@@ -0,0 +1,217 @@
+#######################################################################
+# Copyright (C)                                                       #
+# 2016 - 2019 Pinard Liu([email protected])                      #
+# https://www.cnblogs.com/pinard                                      #
+# Permission given to modify the code as long as you keep this        #
+# declaration at the top                                              #
+#######################################################################
+## reference from MorvanZhou's A3C code on Github, minor update:##
+##https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/contents/10_A3C/A3C_discrete_action.py ##
+
+## https://www.cnblogs.com/pinard/p/10334127.html ##
+## 强化学习(十五) A3C ##
+
+import threading
+import tensorflow as tf
+import numpy as np
+import gym
+import os
+import shutil
+import matplotlib.pyplot as plt
+
+
+GAME = 'CartPole-v0'
+OUTPUT_GRAPH = True
+LOG_DIR = './log'
+N_WORKERS = 3
+MAX_GLOBAL_EP = 3000
+GLOBAL_NET_SCOPE = 'Global_Net'
+UPDATE_GLOBAL_ITER = 100
+GAMMA = 0.9
+ENTROPY_BETA = 0.001
+LR_A = 0.001    # learning rate for actor
+LR_C = 0.001    # learning rate for critic
+GLOBAL_RUNNING_R = []
+GLOBAL_EP = 0
+STEP = 3000 # Step limitation in an episode
+TEST = 10 # The number of experiment test every 100 episode
+
+env = gym.make(GAME)
+N_S = env.observation_space.shape[0]
+N_A = env.action_space.n
+
+
+class ACNet(object):
+    def __init__(self, scope, globalAC=None):
+
+        if scope == GLOBAL_NET_SCOPE:   # 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:   # local net, calculate losses
+            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 + 1e-5) * 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)
+
+            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, globalAC.a_params)]
+                    self.pull_c_params_op = [l_p.assign(g_p) for l_p, g_p in zip(self.c_params, globalAC.c_params)]
+                with tf.name_scope('push'):
+                    self.update_a_op = OPT_A.apply_gradients(zip(self.a_grads, globalAC.a_params))
+                    self.update_c_op = OPT_C.apply_gradients(zip(self.c_grads, globalAC.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 update_global(self, feed_dict):  # run by a local
+        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
+        SESS.run([self.pull_a_params_op, self.pull_c_params_op])
+
+    def choose_action(self, s):  # run by a local
+        prob_weights = 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
+
+
+class Worker(object):
+    def __init__(self, name, globalAC):
+        self.env = gym.make(GAME).unwrapped
+        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
+            while True:
+                # if self.name == 'W_0':
+                #     self.env.render()
+                a = self.AC.choose_action(s)
+                s_, r, done, info = self.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(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.array(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,
+                    }
+                    self.AC.update_global(feed_dict)
+
+                    buffer_s, buffer_a, buffer_r = [], [], []
+                    self.AC.pull_global()
+
+                s = s_
+                total_step += 1
+                if done:
+                    if len(GLOBAL_RUNNING_R) == 0:  # record running episode reward
+                        GLOBAL_RUNNING_R.append(ep_r)
+                    else:
+                        GLOBAL_RUNNING_R.append(0.99 * GLOBAL_RUNNING_R[-1] + 0.01 * ep_r)
+                    print(
+                        self.name,
+                        "Ep:", GLOBAL_EP,
+                        "| Ep_r: %i" % GLOBAL_RUNNING_R[-1],
+                          )
+                    GLOBAL_EP += 1
+                    break
+
+if __name__ == "__main__":
+    SESS = tf.Session()
+
+    with tf.device("/cpu:0"):
+        OPT_A = tf.train.RMSPropOptimizer(LR_A, name='RMSPropA')
+        OPT_C = tf.train.RMSPropOptimizer(LR_C, name='RMSPropC')
+        GLOBAL_AC = ACNet(GLOBAL_NET_SCOPE)  # we only need its params
+        workers = []
+        # Create worker
+        for i in range(N_WORKERS):
+            i_name = 'W_%i' % i   # worker name
+            workers.append(Worker(i_name, GLOBAL_AC))
+
+    COORD = tf.train.Coordinator()
+    SESS.run(tf.global_variables_initializer())
+
+    if OUTPUT_GRAPH:
+        if os.path.exists(LOG_DIR):
+            shutil.rmtree(LOG_DIR)
+        tf.summary.FileWriter(LOG_DIR, SESS.graph)
+
+    worker_threads = []
+    for worker in workers:
+        job = lambda: worker.work()
+        t = threading.Thread(target=job)
+        t.start()
+        worker_threads.append(t)
+    COORD.join(worker_threads)
+
+    testWorker = Worker("test", GLOBAL_AC)
+    testWorker.AC.pull_global()
+
+    total_reward = 0
+    for i in range(TEST):
+        state = env.reset()
+        for j in range(STEP):
+            env.render()
+            action = testWorker.AC.choose_action(state)  # direct action for test
+            state, reward, done, _ = env.step(action)
+            total_reward += reward
+            if done:
+                break
+    ave_reward = total_reward / TEST
+    print('episode: ', GLOBAL_EP, 'Evaluation Average Reward:', ave_reward)
+
+    plt.plot(np.arange(len(GLOBAL_RUNNING_R)), GLOBAL_RUNNING_R)
+    plt.xlabel('step')
+    plt.ylabel('Total moving reward')
+    plt.show()