Policy gradient 最大的一個優勢是: 輸出的這個 action 可以是一個連續的值, 之前我們說到的 value-based 方法輸出的都是不連續的值, 然後再選擇值最大的 action. 而 policy gradient 可以在一個連續分佈上選取 action.
誤差反向傳遞：這種反向傳遞的目的是讓這次被選中的行爲更有可能在下次發生. 但是我們要怎麼確定這個行爲是不是應當被增加被選的概率呢? 這時候我們的老朋友, reward 獎懲正可以在這時候派上用場,
"""
RL_brain for Policy-Gradient-Softmax
"""
import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
np.random.seed(1)
tf.set_random_seed(1)
class PolicyGradient:
def __init__(
self,
n_actions,
n_features,
learning_rate=0.01,
reward_decay=0.95,
output_graph=False,
):
self.n_actions = n_actions
self.n_features = n_features
self.lr = learning_rate
self.gamma = reward_decay
self.ep_obs, self.ep_as, self.ep_rs = [], [], []
self._build_net()
self.sess = tf.Session()
if output_graph:
tf.summary.FileWriter("logs/", self.sess.graph)
self.sess.run(tf.global_variables_initializer())
def _build_net(self):
with tf.name_scope('inputs'):
self.tf_obs = tf.placeholder(tf.float32, [None, self.n_features], name="observations")
self.tf_acts = tf.placeholder(tf.int32, [None, ], name="actions_num")
self.tf_vt = tf.placeholder(tf.float32, [None, ], name="actions_value")
# fc1
layer = tf.layers.dense(
inputs=self.tf_obs,
units=10,
activation=tf.nn.tanh,
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.3),
bias_initializer=tf.constant_initializer(0.1),
name='fc1'
)
# fc2
all_act = tf.layers.dense(
inputs=layer,
units=self.n_actions,
activation=None,
kernel_initializer=tf.random_normal_initializer(mean=0,stddev=0.3),
bias_initializer=tf.constant_initializer(0.1),
name='fc2'
)
self.all_act_prob = tf.nn.softmax(all_act, name='act_prob')
with tf.name_scope('loss'):
# to maximize total reward (log_p * R) is to minimize - (log_p * R), and the tf only have minimize(loss)
neg_log_prob = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=all_act, labels=self.tf_acts)
# or in this way:
# neg_log_prob = tf.reduce_sum(-tf.log(self.all_act_prob)*tf.one_hot(self.tf_acts, self.n_actions), axis=1)
loss = tf.reduce_mean(neg_log_prob * self.tf_vt) # reward guided loss
with tf.name_scope('train'):
self.train_op = tf.train.AdamOptimizer(self.lr).minimize(loss)
def choose_action(self, observation):
prob_weights = self.sess.run(self.all_act_prob, feed_dict={self.tf_obs:observation[np.newaxis, :]})
action = np.random.choice(range(prob_weights.shape[1]), p=prob_weights.ravel()) # select action w.r.t the actions prob
return action
def store_transition(self, s, a, r):
self.ep_obs.append(s)
self.ep_as.append(a)
self.ep_rs.append(r)
def learn(self):
# discount and normalize episode reward
discounted_ep_rs_norm = self._discount_and_norm_rewards()
# train on episode
self.sess.run(self.train_op, feed_dict={
self.tf_obs: np.vstack(self.ep_obs), # shape=[None, n_obs]
self.tf_acts: np.array(self.ep_as), # shape=[None, ]
self.tf_vt: discounted_ep_rs_norm, # shape=[None, ]
})
self.ep_obs, self.ep_as, self.ep_rs = [], [], [] # empty episode data
return discounted_ep_rs_norm
def _discount_and_norm_rewards(self):
"""
按回合爲單位
"""
# discount episode rewards
discounted_ep_rs = np.zeros_like(self.ep_rs)
running_add = 0
for t in reversed(range(0, len(self.ep_rs))):
running_add = running_add * self.gamma + self.ep_rs[t]
discounted_ep_rs[t] = running_add
# normalize episode rewards
discounted_ep_rs -= np.mean(discounted_ep_rs)
discounted_ep_rs /= np.std(discounted_ep_rs)
return discounted_ep_rs
"""
test case 1.
"""
import gym
from RL_brain import PolicyGradient
import matplotlib.pyplot as plt
DISPLAY_REWARD_THRESHOLD = 400 # renders environment if total episode reward is greater than this threshold
RENDER = False # rendering wastes time
env = gym.make('CartPole-v0')
env.seed(1) # reproducible, general Policy gradient has high variance
env = env.unwrapped
print(env.action_space)
print(env.observation_space)
print(env.observation_space.high)
print(env.observation_space.low)
RL = PolicyGradient(
n_actions=env.action_space.n,
n_features=env.observation_space.shape[0],
learning_rate=0.02,
reward_decay=0.99,
# output_graph=True,
)
for i_episode in range(3000):
observation = env.reset()
while True:
if RENDER:env.render()
action = RL.choose_action(observation)
observation_, reward, done, info = env.step(action)
RL.store_transition(observation, action, reward)
if done:
ep_rs_sum = sum(RL.ep_rs)
if 'running_reward' not in globals():
running_reward = ep_rs_sum
else:
running_reward = running_reward * 0.99 + ep_rs_sum * 0.01
if running_reward > DISPLAY_REWARD_THRESHOLD: RENDER = True # rendering
print("episode:", i_episode, " reward:", int(running_reward))
vt = RL.learn()
if i_episode == 0:
plt.plot(vt) # plot the episode vt
plt.xlabel('episode steps')
plt.ylabel('normalized state-action value')
plt.show()
break
observation = observation_
"""
test case 2.
"""
import gym
from RL_brain import PolicyGradient
import matplotlib.pyplot as plt
DISPLAY_REWARD_THRESHOLD = -2000 # renders environment if total episode reward is greater than this threshold
# episode: 154 reward: -10667
# episode: 387 reward: -2009
# episode: 489 reward: -1006
# episode: 628 reward: -502
RENDER = False # rendering wastes time
env = gym.make('MountainCar-v0')
env.seed(1) # reproducible, general Policy gradient has high variance
env = env.unwrapped
print(env.action_space)
print(env.observation_space)
print(env.observation_space.high)
print(env.observation_space.low)
RL = PolicyGradient(
n_actions = env.action_space.n,
n_features = env.observation_space.shape[0],
learning_rate=0.02,
reward_decay=0.995,
# output_graph = True,
)
for i_episode in range(1000):
observation = env.reset()
while True:
if RENDER: env.render()
action = RL.choose_action(observation)
observation_, reward, done, info = env.step(action) # reward = -1 in all cases
RL.store_transition(observation, action, reward)
if done:
# calculate running reward
ep_rs_sum = sum(RL.ep_rs)
if 'running_reward' not in globals():
running_reward = ep_rs_sum
else:
running_reward = running_reward * 0.99 + ep_rs_sum * 0.01
if running_reward > DISPLAY_REWARD_THRESHOLD: RENDER = True # rendering
print("episode:", i_episode, " reward:", int(running_reward))
vt = RL.learn() # train
if i_episode == 30:
plt.plot(vt) # plot the episode vt
plt.xlabel('episode steps')
plt.ylabel('normalized state-action value')
plt.show()
break
observation = observation_