前言
Reinforcement Learning是AI的一个重要方向，本文实现来基于tensorflow的policy based算法
代码
import numpy as np
import tensorflow as tf
import gym
env = gym.make('CartPole-v0')
env.reset()
random_episodes = 0
reward_sum = 0
'''
while random_episodes < 10:
env.render()
observation,reward,done,_ = env.step(np.random.randint(0,2))
reward_sum += reward
if done:
random_episodes += 1
print("Reward for this episode was:",reward_sum)
reward_sum = 0
env.reset
'''
H = 50
batch_size = 25
learning_rate = 1e-1
D = 4
gamma = 0.99
tvars = tf.trainable_variables()
observations = tf.placeholder(tf.float32,[None,D],name="input_x")
w1 = tf.get_variable("w1",shape=[D,H],initializer=tf.contrib.layers.xavier_initializer())
layer1 = tf.nn.relu(tf.matmul(observations,w1))
w2 = tf.get_variable("w2",shape=[H,1],initializer=tf.contrib.layers.xavier_initializer())
score = tf.matmul(layer1,w2)
probability = tf.nn.sigmoid(score)
adam= tf.train.AdamOptimizer(learning_rate=learning_rate)
w1Grad = tf.placeholder(tf.float32,name="batch_grad1")
w2Grad = tf.placeholder(tf.float32,name="batch_grad2")
batchGrad = [w1Grad,w2Grad]
updateGrad = adam.apply_gradients(zip(batchGrad,tvars))
def discount_reward(r):
discount_r = np.zero_like(r)
running_add = 0
for t in reversed(range(r.size)):
running_add = running_add * gamma + r[t]
discount_r[t] = running_add
return discount_r
input_y = tf.placeholder(tf.float32,[None,1],name="input_y")
advantages = tf.placeholder(tf.float32,name="reward_signal")
loglik = tf.log(input_y * (input_y - probability) + (1 - input_y) * (input_y + probability))
loss = -tf.reduce_mean(loglik * advantages)
newGrads = tf.gradients(loss,tvars)
xs,ys,drs = [],[],[]
reward_sum = 0
episode_number = 1
total_episode = 10000
with tf.Session as sess:
rendering = False
init = tf.global_variables_initializer()
sess.run(init)
observation = env.reset()
gradBuffer = sess.run(tvars)
for ix,grad in enumerate(gradBuffer):
gradBuffer[ix] = grad * 0
while episode_number <= total_episode:
if reward_sum/batch_size > 100 or rendering == True:
env.render()
rendering = True
x = np.reshape(observation,[1,D])
tfprob = sess.run(probability,feed_dict={observations:x})
action = 1 if np.random.uniform() < tfprob else 0
xs.qppend(x)
y = 1 - action
ys.append(y)
observation,reward,done,info = env.step(action)
reward_sum += reward
drs.append(reward)
if done:
episode_number += 1
epx = np.vstack(xs)
epy = np.vstack(ys)
epr = np.vstack(drs)
xs,ys,drs = [],[],[]
discounted_epr = discount_reward(epr)
discounted_epr -= np.mean(discounted_epr)
discounted_epr /= np.std(discounted_epr)
tGrad = sess.run(newGrads,feed_dict={observations:epx,input_y:epy,advantages:discounted_epr})
for ix,grad in enumerate(tGrad):
gradBuffer[ix] += grad
if episode_number % batch_size == 0:
sess.run(updateGrad,feed_dict={w1Grad:gradBuffer[0],w2Grad:gradBuffer[1]})
for ix,grad in enumerate(gradBuffer):
gradBuffer[ix] = grad * 0
print('Average reward for episode %d : %f.' %(episode_number,reward_sum/batch_size))
if reward_sum/batch_size > 200:
print("Task solved in",episode_number,'episodes!')
break
reward_sum = 0
observation = env.reset()
