def __init__(self, learning_rate, memory_size, batch_size, sess,
output_size):
self.sess = sess
#state_t
self.encoder_input = tf.placeholder(tf.float32,
shape=[None, n_features],
name='encoder_input')
self.encoder_output = mlp(inputs=self.encoder_input,
n_output=output_size,
scope='encoder_output',
hiddens=[32, 16, 8])
self.decoder_output = mlp(inputs=self.encoder_output,
n_output=n_features,
scope='decoder_output',
hiddens=[8, 16, 32])
self.encoder_output_ = tf.stop_gradient(self.decoder_output)
#some const
self.learning_rate = learning_rate
self.memory_size = memory_size
self.batch_size = batch_size
#memory
self.memory = Memory(self.memory_size)
#for train
self.loss = tf.reduce_mean(
tf.squared_difference(self.encoder_input, self.decoder_output))
self.train = tf.train.AdamOptimizer(self.learning_rate).minimize(
self.loss)
def __init__(
self,
learning_rate,
memory_size,
batch_size,
sess,
output_size
):
self.sess = sess
#state_t
self.encoder_input_t = tf.placeholder(tf.float32, shape=[None, n_features], name='encoder_input_t')
self.encoder_output_t = mlp(inputs=self.encoder_input_t, n_output=output_size, scope='encoder_output_t',
hiddens=[16, 8])
self.encoder_output_t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='encoder_output_t')
self.decoder_output_t = mlp(inputs=self.encoder_output_t, n_output=n_features, scope='decoder_output_t')
self.decoder_output_t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='decoder_output_t')
self.encoder_output_t_ = tf.stop_gradient(self.encoder_output_t)
#state_t+1 tpo->time plus one
self.encoder_input_tpo = tf.placeholder(tf.float32, shape=[None, n_features], name='encoder_input_tpo')
self.encoder_output_tpo = mlp(inputs=self.encoder_input_tpo, n_output=output_size, scope='encoder_output_tpo',
hiddens=[16, 8])
self.encoder_output_tpo_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='encoder_output_tpo')
self.decoder_output_tpo = mlp(inputs=self.encoder_output_tpo, n_output=n_features, scope='decoder_output_tpo')
self.decoder_output_tpo_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='decoder_output_tpo')
self.encoder_output_tpo_ = tf.stop_gradient(self.encoder_output_tpo)
#sync
self.sync_encoder = [tf.assign(x, y) for x, y in zip(self.encoder_output_t_params, self.encoder_output_tpo_params)]
self.sync_decoder = [tf.assign(x, y) for x, y in zip(self.decoder_output_t_params, self.decoder_output_tpo_params)]
#some const
self.learning_rate = learning_rate
self.memory_size = memory_size
self.batch_size = batch_size
#memory
self.memory = Memory(self.memory_size)
#for train
self.loss_0 = tf.reduce_mean(tf.squared_difference(self.encoder_input_t, self.decoder_output_t))
self.loss_1 = tf.reduce_mean(tf.squared_difference(self.encoder_input_tpo,self.decoder_output_tpo))
self.train_0 = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss_0)
self.train_1 = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss_1)
class auto_encoder:
def __init__(
self,
learning_rate,
memory_size,
batch_size,
sess
):
self.sess = sess
self.common_encoder_input = tf.placeholder(tf.float32, shape=[None, n_features], name='common_encoder_input')
self.common_encoder_output = mlp(inputs=self.common_encoder_input, n_output=n_features, scope='common_encoder_output',
hiddens=[16, 8])
self.common_decoder_output = mlp(inputs=self.common_encoder_output, n_output=n_features, scope='common_decoder_output')
self.learning_rate = learning_rate
self.memory_size = memory_size
self.batch_size = batch_size
self.memory = Memory(self.memory_size)
self.loss = tf.reduce_mean(tf.squared_difference(self.common_encoder_input, self.common_decoder_output))
self.train = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)
def update(self):
data = self.memory.sample(self.batch_size)
self.sess.run(self.train, feed_dict = {
self.common_encoder_input: data
})
def store(self, state):
self.memory.store(np.array([state]))
@property
def output(self):
return self.common_encoder_output
@property
def full(self):
return self.memory.return_index() == self.memory_size-1
def __init__(
self,
learning_rate,
memory_size,
batch_size,
sess
):
self.sess = sess
self.common_encoder_input = tf.placeholder(tf.float32, shape=[None, n_features], name='common_encoder_input')
self.common_encoder_output = mlp(inputs=self.common_encoder_input, n_output=n_features, scope='common_encoder_output',
hiddens=[16, 8])
self.common_decoder_output = mlp(inputs=self.common_encoder_output, n_output=n_features, scope='common_decoder_output')
self.learning_rate = learning_rate
self.memory_size = memory_size
self.batch_size = batch_size
self.memory = Memory(self.memory_size)
self.loss = tf.reduce_mean(tf.squared_difference(self.common_encoder_input, self.common_decoder_output))
self.train = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)
class auto_encoder:
def __init__(self, learning_rate, memory_size, batch_size, sess,
output_size):
self.sess = sess
#state_t
self.encoder_input = tf.placeholder(tf.float32,
shape=[None, n_features],
name='encoder_input')
self.encoder_output = mlp(inputs=self.encoder_input,
n_output=output_size,
scope='encoder_output',
hiddens=[32, 16, 8])
self.decoder_output = mlp(inputs=self.encoder_output,
n_output=n_features,
scope='decoder_output',
hiddens=[8, 16, 32])
self.encoder_output_ = tf.stop_gradient(self.decoder_output)
#some const
self.learning_rate = learning_rate
self.memory_size = memory_size
self.batch_size = batch_size
#memory
self.memory = Memory(self.memory_size)
#for train
self.loss = tf.reduce_mean(
tf.squared_difference(self.encoder_input, self.decoder_output))
self.train = tf.train.AdamOptimizer(self.learning_rate).minimize(
self.loss)
def learn(self):
data = self.memory.sample(self.batch_size)
state = []
for i in range(self.batch_size):
state.append(data[i][0])
self.sess.run(self.train, feed_dict={self.encoder_input: state})
def store(self, state):
self.memory.store(np.array([state]))
def output(self, state):
return self.sess.run(self.encoder_output,
feed_dict={self.encoder_input: [np.array(state)]})
def output_loss(self):
data = self.memory.sample(self.batch_size)
state = []
for i in range(self.batch_size):
state.append(data[i][0])
temp = self.sess.run(self.loss, feed_dict={self.encoder_input: state})
print('now loss:', temp)
print(state[0])
temp = self.sess.run(self.decoder_output,
feed_dict={self.encoder_input: state})
print(temp[0])
@property
def full(self):
return self.memory.return_index() == self.memory_size - 1
def process_data(self):
state, action, reward, state_next, done, decays = [], [], [], [], [], []
temp = self.memory.sample(self.batch_size)
for i in range(self.batch_size):
state.append(temp[i][0])
action.append(temp[i][1])
reward.append(temp[i][2])
state_next.append(temp[i][3])
if temp[i][4] == False:
done.append(np.array(0))
else:
done.append(np.array(1))
decays.append(self.decay)
return state, action, reward, state_next, done, decays
# training process
train_rewards_list = []
test_rewards_list = []
show_every_steps = 100
# Exploration parameters
explore_start = 0.9 # exploration probability at start
explore_stop = 0.01 # minimum S probability
decay_rate = 0.0001 # expotentional decay rate for exploration prob
# Network parameters
hidden_size = 20 # number of units in each Q-network hidden layer
learning_rate = 0.01 # Q-network learning rate
# Memory parameters
memory_size = 10000 # memory capacity
batch_size = 32 # experience mini-batch size
pretrain_length = batch_size # number experiences to pretrain the memory
memory = Memory(max_size=memory_size)
# Initialize the simulation
env = gym.make('CartPole-v1')
# TODO 指定网络参数和名字
agent = DQNAgent(env,
explore_start,
explore_stop,
decay_rate,
state_size=state_size,
action_size=action_size,
hidden_size=hidden_size,
use_targetQ=False,
C=20,
use_dueling=False,
class auto_encoder:
def __init__(
self,
learning_rate,
memory_size,
batch_size,
sess,
output_size
):
self.sess = sess
#state_t
self.encoder_input_t = tf.placeholder(tf.float32, shape=[None, n_features], name='encoder_input_t')
self.encoder_output_t = mlp(inputs=self.encoder_input_t, n_output=output_size, scope='encoder_output_t',
hiddens=[16, 8])
self.encoder_output_t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='encoder_output_t')
self.decoder_output_t = mlp(inputs=self.encoder_output_t, n_output=n_features, scope='decoder_output_t')
self.decoder_output_t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='decoder_output_t')
self.encoder_output_t_ = tf.stop_gradient(self.encoder_output_t)
#state_t+1 tpo->time plus one
self.encoder_input_tpo = tf.placeholder(tf.float32, shape=[None, n_features], name='encoder_input_tpo')
self.encoder_output_tpo = mlp(inputs=self.encoder_input_tpo, n_output=output_size, scope='encoder_output_tpo',
hiddens=[16, 8])
self.encoder_output_tpo_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='encoder_output_tpo')
self.decoder_output_tpo = mlp(inputs=self.encoder_output_tpo, n_output=n_features, scope='decoder_output_tpo')
self.decoder_output_tpo_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='decoder_output_tpo')
self.encoder_output_tpo_ = tf.stop_gradient(self.encoder_output_tpo)
#sync
self.sync_encoder = [tf.assign(x, y) for x, y in zip(self.encoder_output_t_params, self.encoder_output_tpo_params)]
self.sync_decoder = [tf.assign(x, y) for x, y in zip(self.decoder_output_t_params, self.decoder_output_tpo_params)]
#some const
self.learning_rate = learning_rate
self.memory_size = memory_size
self.batch_size = batch_size
#memory
self.memory = Memory(self.memory_size)
#for train
self.loss_0 = tf.reduce_mean(tf.squared_difference(self.encoder_input_t, self.decoder_output_t))
self.loss_1 = tf.reduce_mean(tf.squared_difference(self.encoder_input_tpo,self.decoder_output_tpo))
self.train_0 = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss_0)
self.train_1 = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss_1)
def update(self):
data = self.memory.sample(self.batch_size)
#not sure if the data is legal
self.sess.run([self.train_0,self.train_1] feed_dict = {
self.encoder_input_t: data,
self.decoder_output_tpo: data
})
def store(self, state):
self.memory.store(np.array([state]))
@property
def output(self):
return
@property
def full(self):
return self.memory.return_index() == self.memory_size-1
def sync(self):
self.sess.run([self.sync_encoder, self.sync_decoder])
return None
def learn(self):
self.sess.run([self.train_0, self.train_1], feed_dict = {
})
return None
def __init__(self,
n_features,
n_actions,
model,
scope,
sess,
order,
hiddens,
beta,
C,
common_eval_input,
common_target_input,
common_eval_output,
common_target_output,
learning_rate=1e-5,
decay=0.99,
memory_size=20000000,
batch_size=100000,
epsilon_decrement=0.0005,
epsilon_lower=0.2):
self.sess = sess
self.scope = scope
self.n_features = n_features
self.batch_size = batch_size
self.decay = decay
self.model = model
self.memory = Memory(memory_size)
self.order = order
self.beta = beta
self.C = C
self.learn_times = 0
self.epsilon_lower = epsilon_lower
self.epsilon_decrement = epsilon_decrement
self.eval_input = tf.placeholder(tf.float32,
shape=[None, self.n_features],
name='eval_input')
self.target_input = tf.placeholder(tf.float32,
shape=[None, self.n_features],
name='target_input')
self.actions_selected = tf.placeholder(tf.int32,
shape=[
None,
],
name='actions_selected')
self.done = tf.placeholder(tf.float32, shape=[
None,
], name='done')
self.decays = tf.placeholder(tf.float32, shape=[
None,
], name='decay')
self.rewards = tf.placeholder(tf.float32,
shape=[
None,
],
name='rewards')
#about the encoder
self.state_input_t = tf.placeholder(tf.float32,
shape=[None, self.n_features],
name='state_input_t')
self.state_input_tpo = tf.placeholder(tf.float32,
shape=[None, self.n_features],
name='state_input_tpo')
self.action_plus_state_input = tf.placeholder(
tf.float32,
shape=[None, self.n_features + 1],
name='action_plus_state_input')
#share the first layers
self.common_eval_input = common_eval_input
self.common_target_input = common_target_input
self.common_eval_output = common_eval_output
self.common_target_output = common_target_output
with tf.variable_scope(self.scope):
self._epsilon = tf.get_variable(name='epsilon',
dtype=tf.float32,
initializer=1.0)
self._epsilon_decrement = tf.constant(epsilon_decrement)
self.update_epsilon = tf.assign(
self._epsilon, self._epsilon - self._epsilon_decrement)
self.reset_epsilon = tf.assign(self._epsilon, 1)
# self.eval_output = model(inputs=self.eval_input, n_output=n_actions, scope='eval_net', hiddens=hiddens)
# self.target_output = tf.stop_gradient(
# model(inputs=self.target_input, n_output=n_actions, scope='target_net', hiddens=hiddens))
self.eval_output = model(inputs=self.common_eval_output,
n_output=n_actions,
scope='eval_net',
hiddens=hiddens)
self.target_output = tf.stop_gradient(
model(inputs=self.common_target_output,
n_output=n_actions,
scope='target_net',
hiddens=hiddens))
#about encoder
self.encoder_temp_t = mlp(inputs=self.state_input_t,
n_output=64,
scope='encoder_temp_t',
hiddens=[32, 64])
self.encoder_temp_tpo = tf.stop_gradient(
mlp(inputs=self.state_input_tpo,
n_output=64,
scope='encoder_temp_tpo',
hiddens=[32, 64]))
self.encoder_output_t = mlp(inputs=self.encoder_temp_t,
n_output=self.n_features,
scope='encoder_t',
hiddens=[64, 32])
self.encoder_output_tpo = mlp(inputs=self.encoder_temp_tpo,
n_output=self.n_features,
scope='encoder_tpo',
hiddens=[64, 32])
self.predict_output = mlp(inputs=self.action_plus_state_input,
n_output=64,
scope='predict_output',
hiddens=[64, 32])
self.predict_mse = tf.reduce_sum(
tf.square(self.encoder_temp_tpo -
self.predict_output)) * self.n_features
self.emax = tf.get_variable(name='emax',
dtype=tf.float32,
initializer=1.0)
self.update_emax = tf.assign(
self.emax, tf.maximum(self.emax, self.predict_mse))
self.e_normalize = tf.div(self.predict_mse, self.emax)
self.encoder_loss = tf.reduce_sum(
tf.square(self.state_input_t - self.encoder_output_t))
self.train_encoder = tf.train.AdamOptimizer(
learning_rate).minimize(self.encoder_loss)
self.M_loss = self.predict_mse
self.train_M = tf.train.AdamOptimizer(learning_rate).minimize(
self.M_loss)
self.eval_output_selected = tf.reduce_sum(
self.eval_output * tf.one_hot(self.actions_selected, n_actions),
axis=1)
self.eval_output_target = self.rewards + self.decays * tf.reduce_max(
self.target_output, axis=1) * (1. - self.done)
self.loss = tf.reduce_mean(
tf.squared_difference(self.eval_output_selected,
self.eval_output_target))
self.train = tf.train.AdamOptimizer(learning_rate).minimize(self.loss)
self.eval_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=scope + '/eval_net')
self.target_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=scope + '/target_net')
self.update = [
tf.assign(x, y)
for x, y in zip(self.target_params, self.eval_params)
]
self.sess.run(tf.global_variables_initializer())
class DQN:
def __init__(self,
n_features,
n_actions,
model,
scope,
sess,
order,
hiddens,
beta,
C,
common_eval_input,
common_target_input,
common_eval_output,
common_target_output,
learning_rate=1e-5,
decay=0.99,
memory_size=20000000,
batch_size=100000,
epsilon_decrement=0.0005,
epsilon_lower=0.2):
self.sess = sess
self.scope = scope
self.n_features = n_features
self.batch_size = batch_size
self.decay = decay
self.model = model
self.memory = Memory(memory_size)
self.order = order
self.beta = beta
self.C = C
self.learn_times = 0
self.epsilon_lower = epsilon_lower
self.epsilon_decrement = epsilon_decrement
self.eval_input = tf.placeholder(tf.float32,
shape=[None, self.n_features],
name='eval_input')
self.target_input = tf.placeholder(tf.float32,
shape=[None, self.n_features],
name='target_input')
self.actions_selected = tf.placeholder(tf.int32,
shape=[
None,
],
name='actions_selected')
self.done = tf.placeholder(tf.float32, shape=[
None,
], name='done')
self.decays = tf.placeholder(tf.float32, shape=[
None,
], name='decay')
self.rewards = tf.placeholder(tf.float32,
shape=[
None,
],
name='rewards')
#about the encoder
self.state_input_t = tf.placeholder(tf.float32,
shape=[None, self.n_features],
name='state_input_t')
self.state_input_tpo = tf.placeholder(tf.float32,
shape=[None, self.n_features],
name='state_input_tpo')
self.action_plus_state_input = tf.placeholder(
tf.float32,
shape=[None, self.n_features + 1],
name='action_plus_state_input')
#share the first layers
self.common_eval_input = common_eval_input
self.common_target_input = common_target_input
self.common_eval_output = common_eval_output
self.common_target_output = common_target_output
with tf.variable_scope(self.scope):
self._epsilon = tf.get_variable(name='epsilon',
dtype=tf.float32,
initializer=1.0)
self._epsilon_decrement = tf.constant(epsilon_decrement)
self.update_epsilon = tf.assign(
self._epsilon, self._epsilon - self._epsilon_decrement)
self.reset_epsilon = tf.assign(self._epsilon, 1)
# self.eval_output = model(inputs=self.eval_input, n_output=n_actions, scope='eval_net', hiddens=hiddens)
# self.target_output = tf.stop_gradient(
# model(inputs=self.target_input, n_output=n_actions, scope='target_net', hiddens=hiddens))
self.eval_output = model(inputs=self.common_eval_output,
n_output=n_actions,
scope='eval_net',
hiddens=hiddens)
self.target_output = tf.stop_gradient(
model(inputs=self.common_target_output,
n_output=n_actions,
scope='target_net',
hiddens=hiddens))
#about encoder
self.encoder_temp_t = mlp(inputs=self.state_input_t,
n_output=64,
scope='encoder_temp_t',
hiddens=[32, 64])
self.encoder_temp_tpo = tf.stop_gradient(
mlp(inputs=self.state_input_tpo,
n_output=64,
scope='encoder_temp_tpo',
hiddens=[32, 64]))
self.encoder_output_t = mlp(inputs=self.encoder_temp_t,
n_output=self.n_features,
scope='encoder_t',
hiddens=[64, 32])
self.encoder_output_tpo = mlp(inputs=self.encoder_temp_tpo,
n_output=self.n_features,
scope='encoder_tpo',
hiddens=[64, 32])
self.predict_output = mlp(inputs=self.action_plus_state_input,
n_output=64,
scope='predict_output',
hiddens=[64, 32])
self.predict_mse = tf.reduce_sum(
tf.square(self.encoder_temp_tpo -
self.predict_output)) * self.n_features
self.emax = tf.get_variable(name='emax',
dtype=tf.float32,
initializer=1.0)
self.update_emax = tf.assign(
self.emax, tf.maximum(self.emax, self.predict_mse))
self.e_normalize = tf.div(self.predict_mse, self.emax)
self.encoder_loss = tf.reduce_sum(
tf.square(self.state_input_t - self.encoder_output_t))
self.train_encoder = tf.train.AdamOptimizer(
learning_rate).minimize(self.encoder_loss)
self.M_loss = self.predict_mse
self.train_M = tf.train.AdamOptimizer(learning_rate).minimize(
self.M_loss)
self.eval_output_selected = tf.reduce_sum(
self.eval_output * tf.one_hot(self.actions_selected, n_actions),
axis=1)
self.eval_output_target = self.rewards + self.decays * tf.reduce_max(
self.target_output, axis=1) * (1. - self.done)
self.loss = tf.reduce_mean(
tf.squared_difference(self.eval_output_selected,
self.eval_output_target))
self.train = tf.train.AdamOptimizer(learning_rate).minimize(self.loss)
self.eval_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=scope + '/eval_net')
self.target_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=scope + '/target_net')
self.update = [
tf.assign(x, y)
for x, y in zip(self.target_params, self.eval_params)
]
self.sess.run(tf.global_variables_initializer())
def act(self, state):
if random.uniform(0, 1) < self.epsilon:
return random.randint(0, 1)
else:
copy_state = copy.deepcopy(state)
#for debug
#exchange
t = copy_state[self.order]
copy_state[self.order] = copy_state[0]
copy_state[0] = t
action = self.sess.run(
self.eval_output,
feed_dict={self.common_eval_input: np.array([copy_state])})
return np.argmax(action, axis=1)[0].tolist()
def check(self, state):
copy_state = copy.deepcopy(state)
# exchange
t = copy_state[self.order]
copy_state[self.order] = copy_state[0]
copy_state[0] = t
action = self.sess.run(
self.eval_output,
feed_dict={self.common_eval_input: np.array([copy_state])})
return np.argmax(action, axis=1)[0].tolist()
def learn(self):
self.learn_times += 1
state, action, reward, state_next, done, decays = self.process_data()
self.sess.run(self.train,
feed_dict={
self.common_eval_input: state,
self.actions_selected: action,
self.rewards: reward,
self.common_target_input: state_next,
self.done: done,
self.decays: decays
})
if self.epsilon > self.epsilon_lower:
self.sess.run(self.update_epsilon)
if self.learn_times % 10 == 0:
print('start update target network')
self.sess.run(self.update)
def store(self, state, action, reward, state_after, episode_ended):
state_copy = copy.deepcopy(state)
state_after_copy = copy.deepcopy(state_after)
#exchange
t = state_copy[self.order]
state_copy[self.order] = state_copy[0]
state_copy[0] = t
t = state_after_copy[self.order]
state_after_copy[self.order] = state_after_copy[0]
state_after_copy[0] = t
self.memory.store(
np.array(
[state_copy, action, reward, state_after_copy, episode_ended]))
def process_data(self):
state, action, reward, state_next, done, decays = [], [], [], [], [], []
temp = self.memory.sample(self.batch_size)
for i in range(self.batch_size):
state.append(temp[i][0])
action.append(temp[i][1])
reward.append(temp[i][2])
state_next.append(temp[i][3])
if temp[i][4] == False:
done.append(np.array(0))
else:
done.append(np.array(1))
decays.append(self.decay)
return state, action, reward, state_next, done, decays
@property
def epsilon(self):
return self.sess.run(self._epsilon)
def return_new_reward(self, reward, state_t, state_tpo, episode, action):
self.sess.run(self.update_emax,
feed_dict={
self.state_input_t:
np.array([state_t]),
self.state_input_tpo:
np.array([state_tpo]),
self.action_plus_state_input:
np.array([state_t + [action]])
})
temp = self.sess.run(self.e_normalize,
feed_dict={
self.state_input_t:
np.array([state_t]),
self.state_input_tpo:
np.array([state_tpo]),
self.action_plus_state_input:
np.array([state_t + [action]]),
})
return reward + (self.beta / self.C) * temp
def update_M(self):
state, action, reward, state_next, done, decays = self.process_data()
self.sess.run(self.train_M,
feed_dict={
self.state_input_tpo:
state_next,
self.action_plus_state_input:
np.hstack((state, np.array([action]).T))
})
def update_encoder(self):
state, action, reward, state_next, done, decays = self.process_data()
self.sess.run(self.train_encoder,
feed_dict={self.state_input_t: state})
# training process
train_rewards_list = []
test_rewards_list = []
show_every_steps = 100
# Exploration parameters
explore_start = 0.9 # exploration probability at start
explore_stop = 0.01 # minimum S probability
decay_rate = 0.0001 # expotentional decay rate for exploration prob
# Network parameters
hidden_size = 20 # number of units in each Q-network hidden layer
learning_rate = 0.01 # Q-network learning rate
# Memory parameters
memory_size = 10000 # memory capacity
batch_size = 32 # experience mini-batch size
pretrain_length = batch_size # number experiences to pretrain the memory
memory = Memory(max_size=memory_size)
# Initialize the simulation
env = gym.make('CartPole-v1')
# TODO 指定网络参数和模型名字
agent = DQNAgent(env,
explore_start,
explore_stop,
decay_rate,
state_size=state_size,
action_size=action_size,
hidden_size=hidden_size,
use_targetQ=True,
C=20,
use_dueling=False,