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ActorCritic.py
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ActorCritic.py
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import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
import numpy as np
import sys
import json
class Model:
def __init__(self):
tf.reset_default_graph()
self.ep = 0
self.explore = 5
self.boot_strap = 0
self.boot_strap_freq = 5
self.replace_freq = 10
self.target_replace_freq = 10
self.save_freq = 25
self.memory = Memory()
self.policy_net = Net(POLICY_NET)
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())
def get_update(self):
data = self.policy_net.get_weights(self.sess)
data['boot_strap'] = self.boot_strap
data['explore'] = self.explore
data['replace'] = (self.ep % self.replace_freq) == 0
return data
def dump(self):
data = self.policy_net.get_weights(self.sess)
np.save('dump%d.npy' % self.ep,data)
def load(self, file):
print("Loading: " + file, file=sys.stderr)
data = np.load(file).item()
self.policy_net.set_weights( self.sess, data )
print("Sucess", file=sys.stderr)
def run(self,data):
if data['ep'] == self.ep:
print("Duplicate ep %d" % self.ep, file=sys.stderr)
return
self.ep = data['ep']
print("Ep: %d" % self.ep, file=sys.stderr)
for d in self.parse_data(data):
self.memory.insert(d)
if self.memory.full() and np.random.rand() < 0.1:
batch = self.memory.get_batch()
s_t, action, reward = zip(*batch)
s_t = np.array(s_t, dtype=np.float32)
action = np.array(action, dtype=np.float32)
reward = np.array(reward, dtype=np.float32)
_, policy_loss, log_loss = self.sess.run([self.policy_net.optim,
self.policy_net.loss,
self.policy_net.log_loss],
feed_dict={self.policy_net.target : reward,
self.policy_net.s_t : s_t,
self.policy_net.action : action})
print("PLoss: %0.3f" % policy_loss, file=sys.stderr)
self.memory.update_batch_td_error(log_loss)
if not self.memory.full():
print("Experience %d of %d gathered" % (self.memory.curr_idx, self.memory.mem_size), file=sys.stderr)
if self.explore > 1:
self.explore -= 0.001
if self.ep % self.boot_strap_freq == 0:
self.boot_strap = 100
else:
self.boot_strap = 0
if self.ep % self.save_freq == 0:
print("Saving weights", file=sys.stderr)
self.dump()
def parse_data(self,data):
s_t = []
action = []
reward = []
R = 0
for i in reversed(range(len(data) - 1)):
d_t = data[str(i)]
R = d_t['r'] + 0.7*R
s_t.append(d_t['s'])
action.append(d_t['a'])
reward.append(R)
s_t = np.array(s_t, dtype=np.float32)
action = np.array(action, dtype=np.float32)
reward = np.array(reward, dtype=np.float32)
reward = np.clip(reward, -2, 2)
return zip(s_t, action, reward)
VALUE_NET = 0
POLICY_NET = 1
class Net:
def __init__(self, net_type):
self.s_t = tf.placeholder(dtype=tf.float32, shape=[None, 8])
self.var = {}
self.var['W1'] = tf.get_variable('W1',shape=[8,256], initializer=tf.contrib.layers.xavier_initializer())
self.var['b1'] = tf.Variable(tf.zeros([256]), dtype=tf.float32)
self.var['W2'] = tf.get_variable('W2',shape=[256,128], initializer=tf.contrib.layers.xavier_initializer())
self.var['b2'] = tf.Variable(tf.zeros([128]), dtype=tf.float32)
self.var['W3'] = tf.get_variable('W3',shape=[128,60], initializer=tf.contrib.layers.xavier_initializer())
self.var['b3'] = tf.Variable(tf.zeros([60]), dtype=tf.float32)
self.fc1 = tf.nn.relu(tf.matmul(self.s_t, self.var['W1']) + self.var['b1'])
self.fc2 = tf.nn.relu(tf.matmul(self.fc1, self.var['W2']) + self.var['b2'])
self.fc3 = tf.matmul(self.fc2, self.var['W3']) + self.var['b3']
self.pi, self.mu1, self.mu2 = Net.get_mixture_coef(self.fc3)
self.target = tf.placeholder(dtype=tf.float32, shape=[None])
self.action = tf.placeholder(dtype=tf.float32, shape=[None, 2])
self.action_x, self.action_y = tf.split(self.action, num_or_size_splits=2, axis=1)
self.log_loss, self.temp = Net.log_likelihood(self.pi, self.mu1, self.mu2, 5.0, 5.0, 0.0, self.action_x, self.action_y)
self.loss = tf.reduce_mean(self.log_loss * self.target)
self.optim = tf.train.RMSPropOptimizer(
learning_rate=0.001, momentum=0.95, epsilon=0.01).minimize(self.loss)
def get_weights(self, sess):
print("Getting Weights", file=sys.stderr)
data = { k : sess.run(self.var[k]).tolist() for k in self.var.keys() }
return data
def set_weights(self, sess, weights):
print("Setting Weights", file=sys.stderr)
for k,v in weights.items():
var = self.var[k]
value = np.array(v, dtype=np.float32)
sess.run(tf.assign(var, value))
def tf_2d_normal(x1, x2, mu1, mu2, s1, s2, rho):
norm1 = tf.subtract(x1, mu1)
norm2 = tf.subtract(x2, mu2)
s1s2 = tf.multiply(s1, s2)
z = tf.square(tf.div(norm1, s1))+tf.square(tf.div(norm2, s2))-2*tf.div(tf.multiply(rho, tf.multiply(norm1, norm2)), s1s2)
negRho = 1-tf.square(rho)
result = tf.exp(tf.div(-z,2*negRho))
denom = 2*np.pi*tf.multiply(s1s2, tf.sqrt(negRho))
result = tf.div(result, denom)
return result
def log_likelihood(pi, mu1, mu2, sigma1, sigma2, corr, x1_data, x2_data):
result0 = Net.tf_2d_normal(x1_data, x2_data, mu1, mu2, sigma1, sigma2, corr)
result = tf.multiply(result0, pi)
result = tf.reduce_sum(result, axis=1, keep_dims=True)
result = -tf.log(tf.maximum(result, 1e-20)) # at the beginning, some errors are exactly zero.
return result, result0
# below is where we need to do MDN splitting of distribution params
def get_mixture_coef(output):
z = output
z_pi, z_mu1, z_mu2 = tf.split(axis=1, num_or_size_splits=3, value=z)
max_pi = tf.reduce_max(z_pi, axis=1, keep_dims=True)
z_pi = tf.subtract(z_pi, max_pi)
z_pi = tf.exp(z_pi)
normalize_pi = tf.reciprocal(tf.reduce_sum(z_pi, axis=1, keep_dims=True))
z_pi = tf.multiply(normalize_pi, z_pi)
return [z_pi, z_mu1, z_mu2]
class Memory:
def __init__(self):
self.curr_idx = 0
self.mem_size = 1024
self.mem = np.array([None for _ in range(self.mem_size)], dtype=np.object)
self.priority = np.array([0 for _ in range(self.mem_size*2-1)], dtype=np.float32)
self.priority_max = 1000
self.batch_size = 32
self.batch_idx = np.zeros(self.batch_size, dtype=np.int32)
def full(self):
return self.mem[self.curr_idx] != None
def insert(self, data):
self.mem[self.curr_idx] = data
leaf = self.mem_size - 1 + self.curr_idx
self.priority[leaf] = self.priority_max
self.update_priority(leaf)
self.curr_idx = (self.curr_idx + 1) % self.mem_size
def update_priority(self, leaf):
parent = (leaf + 1) // 2 - 1
while parent >= 0:
child_right = (parent + 1) * 2
child_left = child_right - 1
self.priority[parent] = self.priority[child_left] + self.priority[child_right]
parent = (parent + 1) // 2 - 1
def get_batch(self):
batch_priority = np.random.rand(self.batch_size) * self.priority[0]
for i in range(self.batch_size):
priority = batch_priority[i]
parent = 0
while parent < self.mem_size - 1:
child_right = (parent + 1) * 2
child_left = child_right - 1
if priority <= self.priority[child_left]:
parent = child_left
else:
parent = child_right
priority -= self.priority[child_left]
self.batch_idx[i] = parent - self.mem_size + 1
return self.mem[self.batch_idx]
def update_batch_td_error(self, td_error):
priority = np.abs(td_error)
for i in range(self.batch_size):
leaf = self.mem_size - 1 + self.batch_idx[i]
self.priority[leaf] = priority[i]
self.update_priority(leaf)