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test5_lmRL.py
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#MNIST with a BasicLSTMCell
#try tf.app.run and def
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from Agent import Agent
#import argparse
#import sys
import tensorflow as tf
import math
import argparse
import sys
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
class lmmodel(Agent):
def __init__(self,config):
#self._input = input_
super(lmmodel,self).__init__('data/IF1601.CFE.csv', 3, 100, 100)
self.config=config
self.sess = tf.InteractiveSession()
self.buildNetwork()
self.batchsize=100 #batchsize
self.numsteps=120 #120 price sequence
self.hiddensize=20 #20features
self.actionsize=3
init = tf.global_variables_initializer()
self.sess.run(init)
#print(self.get_trajectories())
def choose_action(self, state):
"""Choose an action."""
print(state)
return self.sess.run([self.action], feed_dict={self.states: [state]})
def buildNetwork(self):
with tf.name_scope('input'):
# self.x = tf.placeholder(tf.float32, shape=[100,784], name='x')
# self.y_= tf.placeholder(tf.float32,shape=[100,10],name="y_")
self.states = tf.placeholder(tf.float32,shape=[None,120,20],name= "states")
self.actions_taken = tf.placeholder(tf.int32,shape=[None,3],name= "actions_taken")
self.critic_feedback = tf.placeholder(tf.float32,shape=[ ],name= "critic_feedback")
self.critic_rewards = tf.placeholder(tf.float32,shape=[ ],name= "critic_rewards")
def lstm_cell(size):
return tf.contrib.rnn.BasicLSTMCell(size, forget_bias=0.0, state_is_tuple=True)
# ActorNetwork
with tf.variable_scope("actor") :
L1= tf.contrib.layers.fully_connected(
inputs=self.states,
num_outputs= 20, #hidden
activation_fn= tf.tanh,
weights_initializer = tf.random_normal_initializer(),
biases_initializer = tf.zeros_initializer()
)
print("L1:",L1)
lstmcell=tf.contrib.rnn.BasicLSTMCell(20, forget_bias=0.0, state_is_tuple=True)
cell = tf.contrib.rnn.MultiRNNCell([lstmcell for _ in range(5)], state_is_tuple=True)
#self._initial_state=cell.zero_state(batchsize,tf.float32)
#input = tf.reshape(L1,[100,28,28])
state = cell.zero_state(100,tf.float32) #batchsize*hidden cells
outputs = []
for time_step in range(120):
if time_step > 0: tf.get_variable_scope().reuse_variables()
(cell_output, state) = cell(L1[:,time_step,:], state)
outputs.append(cell_output)
# tf.get_variable_scope().reuse_variables()
output = tf.reshape(tf.concat(axis=1, values=outputs), [-1, 20])
print("output",output)
weights = tf.Variable(tf.truncated_normal([20, 3],stddev=1.0 / math.sqrt(float(1200))),name='weights')
biases = tf.Variable(tf.zeros([3]),name='biases')
logits = tf.matmul(output, weights) + biases
#weights = tf.Variable("softmax_w", [20, 3], dtype=float32)
#softmax_w = tf.get_variable( "softmax_w", [20, 3], dtype=tf.float32)
#softmax_b = tf.get_variable("softmax_b", [3], dtype=tf.float32)
#logits = tf.matmul(output, softmax_w) + softmax_b
self.probs = tf.nn.softmax(logits,name="action")
#print("action",self.action)
#print(tf.shape(self.action))
#self.action =np.amax(self.action0,axis=self.action0[0])
#self.action =tf.multinomial(tf.log(self.action0),1)
#self.action = tf.shape(self.action0)[1]
#gather_indices = tf.range(100) * tf.shape(self.action0)[1] + self.actions_taken
#self.action = tf.gather(tf.reshape(self.action0, [-1]), gather_indices)
#print("action",self.action)
gather_indices = tf.range(tf.shape(self.probs)[0]) * tf.shape(self.probs)[1] + self.actions_taken
self.action = tf.gather(tf.reshape(self.probs, [-1]), gather_indices)
#loss,train
policyloss = tf.log(self.action)*(self.critic_rewards-self.critic_feedback)
loss = tf.negative(tf.reduce_mean(policyloss),name="loss")
with tf.variable_scope("actor-train"):
self.actor_train = tf.train.AdamOptimizer(0.01).minimize(loss)
tf.get_variable_scope().reuse_variables()
# Critic Network
with tf.variable_scope("critic") as scopeB:
self.critic_target = tf.placeholder(tf.float32,name= "critic_target")
critic_L1= tf.contrib.layers.fully_connected(
inputs=self.states,
num_outputs= 20, #hidden
activation_fn= tf.tanh,
weights_initializer = tf.random_normal_initializer(),
biases_initializer = tf.zeros_initializer()
)
# lstmcell=lstm_cell()
lstmcell=tf.contrib.rnn.BasicLSTMCell(20, forget_bias=0.0, state_is_tuple=True)
cell = tf.contrib.rnn.MultiRNNCell([lstmcell for _ in range(5)], state_is_tuple=True)
self._initial_state=cell.zero_state(100,tf.float32)
#scopeB.reuse_variables()
state = cell.zero_state(100,tf.float32) #batchsize*hidden cells
outputs = []
for time_step in range(120):
if time_step > 0: tf.get_variable_scope().reuse_variables()
(cell_output, state) = cell(critic_L1[:,time_step,:], state)
outputs.append(cell_output)
# weights = tf.Variable(tf.truncated_normal([28, 10],stddev=1.0 / math.sqrt(float(28))),name='weights')
# biases = tf.Variable(tf.zeros([10]),name='biases')
# logits = tf.matmul(cell_output, weights) + biases
# self.critic_value = tf.nn.softmax(logits,name="action")
output = tf.reshape(tf.concat(axis=1, values=outputs), [-1, 20])
self.critic_value = tf.contrib.layers.fully_connected(
inputs=output,
num_outputs= 1, #hidden
activation_fn= tf.tanh,
weights_initializer = tf.random_normal_initializer(),
biases_initializer = tf.zeros_initializer()
)
#loss,train
critic_loss = tf.reduce_mean(tf.square(self.critic_target - self.critic_value) , name ="loss" )
self.critic_train = tf.train.AdamOptimizer(0.01).minimize(critic_loss) #global_step
def learn(self):
for iteration in range(1000):
trajectories = self.get_trajectories()
all_action = np.concatenate([trajectory["action"] for trajectory in trajectories])
all_state = np.concatenate([trajectory["state"] for trajectory in trajectories])
# discounted sums of rewards
returns = np.concatenate([trajectory["reward"] for trajectory in trajectories])
#returns = np.concatenate([discount_rewards(trajectory["reward"],"gama") for trajectory in trajectories]) #???
qw_new = self.session.run([self.critic_value],feed_dict={self.states:[all_state]}) #???
#episode_rewards = np.concatenate([trajectory["reward"].sum() for trajectory in trajectories])
#episode_length = np.concatenate([len(trajectory["reward"]) for trajectory in trajectories]) ##???
results = self.session.run([self.critic_train,self.actor_train],feed_dict={
self.states: all_state,
self.critic_target:returns,
self.states: all_state,
self.actions_taken: all_action,
self.critic_feedback:qw_new,
self.critic_rewards:returns
})
class config(object):
learning_rate= 1.0
num_layers =2
num_steps= 20
hidden_size = 28
batch_size=100
number=1000
def get_config():
return config()
def main():
# testAgent = Agent('data/IF1601.CFE.csv', 3, 5, 2)
# mnist = input_data.read_data_sets('/tmp/tensorflow/mnist/input_data',one_hot=True)
# train_input,ys = mnist.train.next_batch(100)
config=get_config()
with tf.name_scope("train"):
out = lmmodel(config=config)
# out.buildNetwork()
out.learn()
# with tf.variable_scope("Model",reuse=True):
#for i in range(config.number):
# if i%10 ==0:
# acc= sess.run(out.accuracy,feed_dict=feed_dict())
# print('Accuracy at step %s: %s'%(i,acc))
#out=sess.run(out.train_step,feed_dict=feed_dict())
if __name__ == '__main__':
main()
#tf.app.run()