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Random_Network_Distillation.py
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Random_Network_Distillation.py
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"""This is a simple implementation of [Exploration by Random Network Distillation](https://arxiv.org/abs/1810.12894)"""
import numpy as np
import tensorflow as tf
import gym
import matplotlib.pyplot as plt
class CuriosityNet:
def __init__(
self,
n_a,
n_s,
lr=0.01,
gamma=0.95,
epsilon=1.,
replace_target_iter=300,
memory_size=10000,
batch_size=128,
output_graph=False,
):
self.n_a = n_a
self.n_s = n_s
self.lr = lr
self.gamma = gamma
self.epsilon = epsilon
self.replace_target_iter = replace_target_iter
self.memory_size = memory_size
self.batch_size = batch_size
self.s_encode_size = 1000 # give a hard job for predictor to learn
# total learning step
self.learn_step_counter = 0
self.memory_counter = 0
# initialize zero memory [s, a, r, s_]
self.memory = np.zeros((self.memory_size, n_s * 2 + 2))
self.tfs, self.tfa, self.tfr, self.tfs_, self.pred_train, self.dqn_train, self.q = \
self._build_nets()
t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='target_net')
e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='eval_net')
with tf.variable_scope('hard_replacement'):
self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]
self.sess = tf.Session()
if output_graph:
tf.summary.FileWriter("logs/", self.sess.graph)
self.sess.run(tf.global_variables_initializer())
def _build_nets(self):
tfs = tf.placeholder(tf.float32, [None, self.n_s], name="s") # input State
tfa = tf.placeholder(tf.int32, [None, ], name="a") # input Action
tfr = tf.placeholder(tf.float32, [None, ], name="ext_r") # extrinsic reward
tfs_ = tf.placeholder(tf.float32, [None, self.n_s], name="s_") # input Next State
# fixed random net
with tf.variable_scope("random_net"):
rand_encode_s_ = tf.layers.dense(tfs_, self.s_encode_size)
# predictor
ri, pred_train = self._build_predictor(tfs_, rand_encode_s_)
# normal RL model
q, dqn_loss, dqn_train = self._build_dqn(tfs, tfa, ri, tfr, tfs_)
return tfs, tfa, tfr, tfs_, pred_train, dqn_train, q
def _build_predictor(self, s_, rand_encode_s_):
with tf.variable_scope("predictor"):
net = tf.layers.dense(s_, 128, tf.nn.relu)
out = tf.layers.dense(net, self.s_encode_size)
with tf.name_scope("int_r"):
ri = tf.reduce_sum(tf.square(rand_encode_s_ - out), axis=1) # intrinsic reward
train_op = tf.train.RMSPropOptimizer(self.lr, name="predictor_opt").minimize(
tf.reduce_mean(ri), var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "predictor"))
return ri, train_op
def _build_dqn(self, s, a, ri, re, s_):
with tf.variable_scope('eval_net'):
e1 = tf.layers.dense(s, 128, tf.nn.relu)
q = tf.layers.dense(e1, self.n_a, name="q")
with tf.variable_scope('target_net'):
t1 = tf.layers.dense(s_, 128, tf.nn.relu)
q_ = tf.layers.dense(t1, self.n_a, name="q_")
with tf.variable_scope('q_target'):
q_target = re + ri + self.gamma * tf.reduce_max(q_, axis=1, name="Qmax_s_")
with tf.variable_scope('q_wrt_a'):
a_indices = tf.stack([tf.range(tf.shape(a)[0], dtype=tf.int32), a], axis=1)
q_wrt_a = tf.gather_nd(params=q, indices=a_indices)
loss = tf.losses.mean_squared_error(labels=q_target, predictions=q_wrt_a) # TD error
train_op = tf.train.RMSPropOptimizer(self.lr, name="dqn_opt").minimize(
loss, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "eval_net"))
return q, loss, train_op
def store_transition(self, s, a, r, s_):
transition = np.hstack((s, [a, r], s_))
# replace the old memory with new memory
index = self.memory_counter % self.memory_size
self.memory[index, :] = transition
self.memory_counter += 1
def choose_action(self, observation):
# to have batch dimension when feed into tf placeholder
s = observation[np.newaxis, :]
if np.random.uniform() < self.epsilon:
# forward feed the observation and get q value for every actions
actions_value = self.sess.run(self.q, feed_dict={self.tfs: s})
action = np.argmax(actions_value)
else:
action = np.random.randint(0, self.n_a)
return action
def learn(self):
# check to replace target parameters
if self.learn_step_counter % self.replace_target_iter == 0:
self.sess.run(self.target_replace_op)
# sample batch memory from all memory
top = self.memory_size if self.memory_counter > self.memory_size else self.memory_counter
sample_index = np.random.choice(top, size=self.batch_size)
batch_memory = self.memory[sample_index, :]
bs, ba, br, bs_ = batch_memory[:, :self.n_s], batch_memory[:, self.n_s], \
batch_memory[:, self.n_s + 1], batch_memory[:, -self.n_s:]
self.sess.run(self.dqn_train, feed_dict={self.tfs: bs, self.tfa: ba, self.tfr: br, self.tfs_: bs_})
if self.learn_step_counter % 100 == 0: # delay training in order to stay curious
self.sess.run(self.pred_train, feed_dict={self.tfs_: bs_})
self.learn_step_counter += 1
env = gym.make('MountainCar-v0')
env = env.unwrapped
dqn = CuriosityNet(n_a=3, n_s=2, lr=0.01, output_graph=False)
ep_steps = []
for epi in range(200):
s = env.reset()
steps = 0
while True:
# env.render()
a = dqn.choose_action(s)
s_, r, done, info = env.step(a)
dqn.store_transition(s, a, r, s_)
dqn.learn()
if done:
print('Epi: ', epi, "| steps: ", steps)
ep_steps.append(steps)
break
s = s_
steps += 1
plt.plot(ep_steps)
plt.ylabel("steps")
plt.xlabel("episode")
plt.show()