add ddpg code

Author: pinard.liu

reinforcement-learning/ddpg.py

@@ -0,0 +1,173 @@
+"""
+Deep Deterministic Policy Gradient (DDPG), Reinforcement Learning.
+DDPG is Actor Critic based algorithm.
+Pendulum example.
+View more on my tutorial page: https://morvanzhou.github.io/tutorials/
+Using:
+tensorflow 1.0
+gym 0.8.0
+"""
+#######################################################################
+# 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                                              #
+#######################################################################
+
+## https://www.cnblogs.com/pinard/p/10345762.html.html ##
+## 强化学习(十六) 深度确定性策略梯度(DDPG) ##
+
+
+import tensorflow as tf
+import numpy as np
+import gym
+import time
+
+
+#####################  hyper parameters  ####################
+
+MAX_EPISODES = 2000
+MAX_EP_STEPS = 200
+LR_A = 0.001    # learning rate for actor
+LR_C = 0.002    # learning rate for critic
+GAMMA = 0.9     # reward discount
+TAU = 0.01      # soft replacement
+MEMORY_CAPACITY = 10000
+BATCH_SIZE = 32
+
+RENDER = False
+ENV_NAME = 'Pendulum-v0'
+
+###############################  DDPG  ####################################
+
+class DDPG(object):
+    def __init__(self, a_dim, s_dim, a_bound,):
+        self.memory = np.zeros((MEMORY_CAPACITY, s_dim * 2 + a_dim + 1), dtype=np.float32)
+        self.pointer = 0
+        self.sess = tf.Session()
+
+        self.a_dim, self.s_dim, self.a_bound = a_dim, s_dim, a_bound,
+        self.S = tf.placeholder(tf.float32, [None, s_dim], 's')
+        self.S_ = tf.placeholder(tf.float32, [None, s_dim], 's_')
+        self.R = tf.placeholder(tf.float32, [None, 1], 'r')
+
+        with tf.variable_scope('Actor'):
+            self.a = self._build_a(self.S, scope='eval', trainable=True)
+            a_ = self._build_a(self.S_, scope='target', trainable=False)
+        with tf.variable_scope('Critic'):
+            # assign self.a = a in memory when calculating q for td_error,
+            # otherwise the self.a is from Actor when updating Actor
+            q = self._build_c(self.S, self.a, scope='eval', trainable=True)
+            q_ = self._build_c(self.S_, a_, scope='target', trainable=False)
+
+        # networks parameters
+        self.ae_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Actor/eval')
+        self.at_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Actor/target')
+        self.ce_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Critic/eval')
+        self.ct_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Critic/target')
+
+        # target net replacement
+        self.soft_replace = [tf.assign(t, (1 - TAU) * t + TAU * e)
+                             for t, e in zip(self.at_params + self.ct_params, self.ae_params + self.ce_params)]
+
+        q_target = self.R + GAMMA * q_
+        # in the feed_dic for the td_error, the self.a should change to actions in memory
+        td_error = tf.losses.mean_squared_error(labels=q_target, predictions=q)
+        self.ctrain = tf.train.AdamOptimizer(LR_C).minimize(td_error, var_list=self.ce_params)
+
+        a_loss = - tf.reduce_mean(q)    # maximize the q
+        self.atrain = tf.train.AdamOptimizer(LR_A).minimize(a_loss, var_list=self.ae_params)
+
+        self.sess.run(tf.global_variables_initializer())
+
+    def choose_action(self, s):
+        return self.sess.run(self.a, {self.S: s[np.newaxis, :]})[0]
+
+    def learn(self):
+        # soft target replacement
+        self.sess.run(self.soft_replace)
+
+        indices = np.random.choice(MEMORY_CAPACITY, size=BATCH_SIZE)
+        bt = self.memory[indices, :]
+        bs = bt[:, :self.s_dim]
+        ba = bt[:, self.s_dim: self.s_dim + self.a_dim]
+        br = bt[:, -self.s_dim - 1: -self.s_dim]
+        bs_ = bt[:, -self.s_dim:]
+
+        self.sess.run(self.atrain, {self.S: bs})
+        self.sess.run(self.ctrain, {self.S: bs, self.a: ba, self.R: br, self.S_: bs_})
+
+    def store_transition(self, s, a, r, s_):
+        transition = np.hstack((s, a, [r], s_))
+        index = self.pointer % MEMORY_CAPACITY  # replace the old memory with new memory
+        self.memory[index, :] = transition
+        self.pointer += 1
+
+    def _build_a(self, s, scope, trainable):
+        with tf.variable_scope(scope):
+            net = tf.layers.dense(s, 30, activation=tf.nn.relu, name='l1', trainable=trainable)
+            a = tf.layers.dense(net, self.a_dim, activation=tf.nn.tanh, name='a', trainable=trainable)
+            return tf.multiply(a, self.a_bound, name='scaled_a')
+
+    def _build_c(self, s, a, scope, trainable):
+        with tf.variable_scope(scope):
+            n_l1 = 30
+            w1_s = tf.get_variable('w1_s', [self.s_dim, n_l1], trainable=trainable)
+            w1_a = tf.get_variable('w1_a', [self.a_dim, n_l1], trainable=trainable)
+            b1 = tf.get_variable('b1', [1, n_l1], trainable=trainable)
+            net = tf.nn.relu(tf.matmul(s, w1_s) + tf.matmul(a, w1_a) + b1)
+            return tf.layers.dense(net, 1, trainable=trainable)  # Q(s,a)
+
+###############################  training  ####################################
+
+env = gym.make(ENV_NAME)
+env = env.unwrapped
+env.seed(1)
+
+s_dim = env.observation_space.shape[0]
+a_dim = env.action_space.shape[0]
+a_bound = env.action_space.high
+
+ddpg = DDPG(a_dim, s_dim, a_bound)
+
+var = 3  # control exploration
+t1 = time.time()
+for episode in range(MAX_EPISODES):
+    s = env.reset()
+    ep_reward = 0
+    for j in range(MAX_EP_STEPS):
+        if RENDER:
+            env.render()
+
+        # Add exploration noise
+        a = ddpg.choose_action(s)
+        a = np.clip(np.random.normal(a, var), -2, 2)    # add randomness to action selection for exploration
+        s_, r, done, info = env.step(a)
+
+        ddpg.store_transition(s, a, r / 10, s_)
+
+        if ddpg.pointer > MEMORY_CAPACITY:
+            var *= .9995    # decay the action randomness
+            ddpg.learn()
+
+        s = s_
+        ep_reward += r
+        if j == MAX_EP_STEPS-1:
+            print('Episode:', episode, ' Reward: %i' % int(ep_reward), 'Explore: %.2f' % var, )
+            # if ep_reward > -300:RENDER = True
+            break
+    if episode % 100 == 0:
+      total_reward = 0
+      for i in range(10):
+        state = env.reset()
+        for j in range(MAX_EP_STEPS):
+          env.render()
+          action = ddpg.choose_action(state) # direct action for test
+          state,reward,done,_ = env.step(action)
+          total_reward += reward
+          if done:
+            break
+      ave_reward = total_reward/300
+      print ('episode: ',episode,'Evaluation Average Reward:',ave_reward)
+print('Running time: ', time.time() - t1)