Matlab系列--还在整理中..尝试将强化学习（DDPG框架）用于锌冶炼净化过程的锌粉添加量优化.。首先在matl.zip

from tensorflow.contrib.layers.python.layers import batch_norm as batch_norm import tensorflow as tf import numpy as np import math LAYER1_SIZE = 400 LAYER2_SIZE = 300 LEARNING_RATE = 1e-3 TAU = 0.001 L2 = 0.01 class CriticNetwork: """docstring for CriticNetwork""" def __init__(self,sess,state_dim,action_dim): self.time_step = 0 self.sess = sess # create q network self.state_input,\ self.action_input,\ self.q_value_output,\ self.net,\ self.is_training = self.create_q_network(state_dim,action_dim) # create target q network (the same structure with q network) self.target_state_input,\ self.target_action_input,\ self.target_q_value_output,\ self.target_update,\ self.target_is_training = self.create_target_q_network(state_dim,action_dim,self.net) self.create_training_method() # initialization self.sess.run(tf.initialize_all_variables()) self.update_target() def create_training_method(self): # Define training optimizer self.y_input = tf.placeholder("float",[None,1]) weight_decay = tf.add_n([L2 * tf.nn.l2_loss(var) for var in self.net]) self.cost = tf.reduce_mean(tf.square(self.y_input - self.q_value_output)) + weight_decay self.optimizer = tf.train.AdamOptimizer(LEARNING_RATE).minimize(self.cost) self.action_gradients = tf.gradients(self.q_value_output,self.action_input) def create_q_network(self,state_dim,action_dim): # the layer size could be changed layer1_size = LAYER1_SIZE layer2_size = LAYER2_SIZE state_input = tf.placeholder("float",[None,state_dim]) action_input = tf.placeholder("float",[None,action_dim]) is_training = tf.placeholder(tf.bool) W1 = self.variable([state_dim,layer1_size],state_dim) b1 = self.variable([layer1_size],state_dim) W2 = self.variable([layer1_size,layer2_size],layer1_size+action_dim) W2_action = self.variable([action_dim,layer2_size],layer1_size+action_dim) b2 = self.variable([layer2_size],layer1_size+action_dim) W3 = tf.Variable(tf.random_uniform([layer2_size,1],-3e-3,3e-3)) b3 = tf.Variable(tf.random_uniform([1],-3e-3,3e-3)) layer0_bn = self.batch_norm_layer(state_input,training_phase=is_training,scope_bn='q_batch_norm_0',activation=tf.identity) layer1 = tf.nn.relu(tf.matmul(layer0_bn,W1) + b1) layer2 = tf.nn.relu(tf.matmul(layer1,W2) + tf.matmul(action_input,W2_action) + b2) q_value_output = tf.identity(tf.matmul(layer2,W3) + b3) return state_input,action_input,q_value_output,[W1,b1,W2,W2_action,b2,W3,b3],is_training def create_target_q_network(self,state_dim,action_dim,net): state_input = tf.placeholder("float",[None,state_dim]) action_input = tf.placeholder("float",[None,action_dim]) is_training = tf.placeholder(tf.bool) ema = tf.train.ExponentialMovingAverage(decay=1-TAU) target_update = ema.apply(net) target_net = [ema.average(x) for x in net] layer0_bn = self.batch_norm_layer(state_input,training_phase=is_training,scope_bn='target_q_batch_norm_0',activation=tf.identity) layer1 = tf.nn.relu(tf.matmul(layer0_bn,target_net[0]) + target_net[1]) layer2 = tf.nn.relu(tf.matmul(layer1,target_net[2]) + tf.matmul(action_input,target_net[3]) + target_net[4]) q_value_output = tf.identity(tf.matmul(layer2,target_net[5]) + target_net[6]) return state_input,action_input,q_value_output,target_update,is_training def update_target(self): self.sess.run(self.target_update) def train(self,y_batch,state_batch,action_batch): self.time_step += 1 self.sess.run(self.optimizer,feed_dict={ self.y_input:y_batch, self.state_input:state_batch, self.action_input:action_batch, self.is_training: True }) def gradients(self,state_batch,action_batch): return self.sess.run(self.action_gradients,feed_dict={ self.state_input:state_batch, self.action_input:action_batch, self.is_training: False })[0] def target_q(self,state_batch,action_batch): return self.sess.run(self.target_q_value_output,feed_dict={ self.target_state_input:state_batch, self.target_action_input:action_batch, self.target_is_training: False }) def q_value(self,state_batch,action_batch): return self.sess.run(self.q_value_output,feed_dict={ self.state_input:state_batch, self.action_input:action_batch, self.is_training: False}) # f fan-in size def variable(self,shape,f): return tf.Variable(tf.random_uniform(shape,-1/math.sqrt(f),1/math.sqrt(f))) def batch_norm_layer(self,x,training_phase,scope_bn,activation=None): return tf.cond(training_phase, lambda: tf.contrib.layers.batch_norm(x, activation_fn=activation, center=True, scale=True, updates_collections=None,is_training=True, reuse=None,scope=scope_bn,decay=0.9, epsilon=1e-5), lambda: tf.contrib.layers.batch_norm(x, activation_fn =activation, center=True, scale=True, updates_collections=None,is_training=False, reuse=True,scope=scope_bn,decay=0.9, epsilon=1e-5)) ''' def load_network(self): self.saver = tf.train.Saver() checkpoint = tf.train.get_checkpoint_state("saved_critic_networks") if checkpoint and checkpoint.model_checkpoint_path: self.saver.restore(self.sess, checkpoint.model_checkpoint_path) print "Successfully loaded:", checkpoint.model_checkpoint_path else: print "Could not find old network weights" def save_network(self,time_step): print 'save critic-network...',time_step self.saver.save(self.sess, 'saved_critic_networks/' + 'critic-network', global_step = time_step) '''