def train(self):
"""train"""
inputs = self.model.create_inputs(mode='train')
output_dict = self.model.forward(inputs, mode='train')
total_loss = 0
if 'click' in self._output_type:
click_id = inputs['click_id']
click_prob = output_dict['click_prob']
click_loss = layers.reduce_mean(
layers.cross_entropy(input=click_prob, label=click_id))
total_loss += click_loss
if 'credit' in self._output_type:
credit = inputs['credit'] * self._credit_scale
credit_pred = output_dict['credit_pred']
credit_loss = layers.reduce_mean(
layers.square_error_cost(input=credit_pred, label=credit))
total_loss += credit_loss
if 'rate' in self._output_type:
rate = layers.cast(inputs['click_id'],
'float32') * self._rate_scale
rate_pred = output_dict['rate_pred']
rate_loss = layers.reduce_mean(
layers.square_error_cost(input=rate_pred, label=rate))
total_loss += rate_loss
if self.optimizer == 'Adam':
optimizer = fluid.optimizer.Adam(learning_rate=self.lr,
epsilon=1e-4)
elif self.optimizer == 'SGD':
optimizer = fluid.optimizer.SGD(learning_rate=self.lr)
optimizer.minimize(total_loss)
fetch_dict = OrderedDict()
fetch_dict[
'loss'] = total_loss # don't rename 'loss', which will be used in parallel exe in computational task
if 'click' in self._output_type:
fetch_dict['click_prob'] = click_prob
fetch_dict['click_id'] = click_id
fetch_dict['click_loss'] = click_loss
if 'credit' in self._output_type:
fetch_dict['credit_pred'] = credit_pred / self._credit_scale
fetch_dict['credit'] = credit / self._credit_scale
fetch_dict['credit_loss'] = credit_loss
if 'rate' in self._output_type:
fetch_dict['rate_pred'] = rate_pred / self._rate_scale
fetch_dict['rate'] = rate / self._rate_scale
fetch_dict['rate_loss'] = rate_loss
return {'fetch_dict': fetch_dict}
def learn(self, obs, action, reward, next_obs, terminal):
""" 使用DQN算法更新self.model的value网络
"""
# 从target_model中获取 max Q' 的值，用于计算target_Q
next_pred_value = self.target_model.value(next_obs)
best_v = layers.reduce_max(next_pred_value, dim=1)
best_v.stop_gradient = True # 阻止梯度传递
terminal = layers.cast(terminal, dtype='float32')
target = reward + (1.0 - terminal) * self.gamma * best_v
pred_value = self.model.value(obs) # 获取Q预测值
# 将action转onehot向量，比如：3 => [0,0,0,1,0]，独热编码有好处
action_onehot = layers.one_hot(action, self.act_dim)
action_onehot = layers.cast(action_onehot, dtype='float32')
# 下面一行是逐元素相乘，拿到action对应的 Q(s,a)
# 比如：pred_value = [[2.3, 5.7, 1.2, 3.9, 1.4]], action_onehot = [[0,0,0,1,0]]
# ==> pred_action_value = [[3.9]]
pred_action_value = layers.reduce_sum(layers.elementwise_mul(
action_onehot, pred_value),
dim=1)
# 计算 Q(s,a) 与 target_Q的均方差，得到loss
cost = layers.square_error_cost(pred_action_value, target)
cost = layers.reduce_mean(cost)
optimizer = fluid.optimizer.Adam(learning_rate=self.lr) # 使用Adam优化器
optimizer.minimize(cost)
return cost
def define_learn(self, obs, action, reward, next_obs, terminal, weight):
#Q(s,a|θ)
pred_value = self.model.value(obs)
#Q(s',a'|θ')
targetQ_predict_value = self.target_model.value(next_obs)
#Q(s',a'|θ)
next_s_predcit_value = self.model.value(next_obs)
#argMax[Q(s',a'|θ)]
greedy_action = fluid_argmax(next_s_predcit_value)
predict_onehot = fluid.layers.one_hot(greedy_action, self.action_dim)
#Q(s',argMax[Q(s',a'|θ)]|θ')
best_v = fluid.layers.reduce_sum(fluid.layers.elementwise_mul(
predict_onehot, targetQ_predict_value),
dim=1)
best_v.stop_gradient = True
#TD目标: R+γ*Q(s',argMax[Q(s',a'|θ)]|θ')
target = reward + (
1.0 - layers.cast(terminal, dtype='float32')) * self.gamma * best_v
action_onehot = layers.one_hot(action, self.action_dim)
action_onehot = layers.cast(action_onehot, dtype='float32')
pred_action_value = layers.reduce_sum(layers.elementwise_mul(
action_onehot, pred_value),
dim=1)
#计算新的TD-Error
newTd = layers.abs(target - pred_action_value)
cost = layers.square_error_cost(pred_action_value, target)
#weight表示样本的权重，影响cost的更新幅度
cost = weight * cost
cost = layers.reduce_mean(cost)
optimizer = fluid.optimizer.Adam(self.lr, epsilon=1e-3)
optimizer.minimize(cost)
return cost, newTd
def train_critic(inputs, click_id):
output_dict = self.model.forward(inputs, output_type='c_Q')
c_Q = output_dict['Q']
target_Q = self.get_target_Q(inputs, click_id)
target_Q.stop_gradient = True
critic_loss = layers.reduce_mean(layers.square_error_cost(c_Q, target_Q))
if self.optimizer == 'Adam':
optimizer = fluid.optimizer.Adam(learning_rate=self.lr, epsilon=1e-4)
elif self.optimizer == 'SGD':
optimizer = fluid.optimizer.SGD(learning_rate=self.lr)
optimizer.minimize(critic_loss)
return critic_loss
def _critic_learn(self, obs, action, reward, next_obs, terminal):
next_action = self.target_model.policy(next_obs)
next_Q = self.target_model.value(next_obs, next_action)
terminal = layers.cast(terminal, dtype='float32')
target_Q = reward + (1.0 - terminal) * self.gamma * next_Q
target_Q.stop_gradient = True
Q = self.model.value(obs, action)
cost = layers.square_error_cost(Q, target_Q)
cost = layers.reduce_mean(cost)
optimizer = fluid.optimizer.AdamOptimizer(self.critic_lr)
optimizer.minimize(cost)
return cost
def test(self):
"""test"""
inputs = self.model.create_inputs(mode='train')
click_id = layers.cast(inputs['click_id'], 'float32') * self._reward_scale
output_dict = self.model.forward(inputs, output_type='c_Q')
c_Q = output_dict['Q']
target_Q = self.get_target_Q(inputs, click_id)
loss = layers.reduce_mean(layers.square_error_cost(c_Q, target_Q))
fetch_dict = OrderedDict()
fetch_dict['loss'] = loss
fetch_dict['c_Q'] = c_Q / self._reward_scale
fetch_dict['click_id'] = click_id / self._reward_scale
return {'fetch_dict': fetch_dict}
def test(self):
"""test"""
inputs = self.model.create_inputs(mode='train')
reward = layers.cast(inputs['reward'], 'float32')
c_Q = self.model.forward(inputs, output_type='c_Q')
max_Q = self.target_model.forward(inputs, output_type='max_Q')
target_Q = self.get_target_Q(max_Q, reward)
loss = layers.reduce_mean(layers.square_error_cost(c_Q, target_Q))
fetch_dict = OrderedDict()
fetch_dict['loss'] = loss
fetch_dict['c_Q'] = c_Q
fetch_dict['reward'] = reward
return {'fetch_dict': fetch_dict}
def learn(self, obs, action, reward, next_obs, terminal):
next_pred_value = self.target_model.value(next_obs)
best_v = layers.reduce_max(next_pred_value, dim=-1)
best_v.stop_gradient = True
terminal = layers.cast(terminal, dtype="float32")
target = reward + (1.0 - terminal) * self.gamma * best_v
pred_value = self.model.value(obs)
action_onehot = layers.one_hot(action, self.act_dim)
action_onehot = layers.cast(action_onehot, dtype="float32")
pred_action_value = layers.reduce_sum(layers.elementwise_mul(
pred_value, action_onehot),
dim=-1)
cost = layers.square_error_cost(target, pred_action_value)
cost = layers.reduce_mean(cost)
optimizer = fluid.optimizer.Adam(learning_rate=self.lr)
optimizer.minimize(cost)
return cost
def learn(self, obs, action, reward, next_obs, terminal):
'''
:param obs: St
:param action: At
:param reward: Rt+1
:param next_obs: St+1
:param terminal: done, True代表episode结束
:return: 损失函数的值
'''
# 通过目标网络计算得到target_Q的值
target_Q_tensor = self.target_model.value(next_obs) # 计算St+1对应的价值向量
max_Q = layers.reduce_max(target_Q_tensor, dim=1) # 获取每行的最大值，按dim=1收缩
max_Q.stop_gradient = True # 停止梯度更新
# 由于terminal不是标量，所以不能直接用判断
terminal = layers.cast(terminal, dtype="float32")
target_Q = reward + (1.0 - terminal) * self.gamma * max_Q
# 通过主网络计算得到perdict_Q的值
predict_Q_tensor = self.model.value(obs)
# 将action转成one-hot向量，并将每一位都变成浮点数
action_onehot = layers.one_hot(action, self.act_dim)
action = layers.cast(action_onehot, dtype="float32")
# 进行elementwise计算并降低张量阶数
# 比如 predict_Q_tensor = [[2.3, 5.7, 1.2, 3.9, 1.4], action_onehot=[[0, 0, 0, 1, 0]
# [2.1, 3.7, 4.5, 6.7, 7.1]] [0, 1, 0, 0, 0]]
# 那么elementwise乘法运算后的结果是 [[0, 0, 0, 3.9, 0]
# [0, 3.7, 0, 0, 0]]
# 再进行dim=1的reduce_sum操作后的结果是 [3.9, 3.7]
predict_Q = layers.reduce_sum(layers.elementwise_mul(
action_onehot, predict_Q_tensor),
dim=1)
# 得到这个batch每条数据的损失函数值的平均值
cost = layers.square_error_cost(predict_Q, target_Q)
cost = layers.reduce_mean(cost)
# 申明优化器（使用Adam优化器）
optimizer = fluid.optimizer.Adam(learning_rate=self.lr)
optimizer.minimize(cost) # 指定优化目标
return cost
def _critic_learn(self, obs, action, reward, next_obs, terminal):
next_action = self.target_model.policy(next_obs)
next_Q = self.target_model.value(next_obs, next_action)
terminal = layers.cast(terminal, dtype='float32')
target_Q = reward + (1.0 - terminal) * self.gamma * next_Q
target_Q.stop_gradient = True
Q = self.model.value(obs, action)
cost = layers.square_error_cost(Q, target_Q)
cost = layers.reduce_mean(cost)
# optimizer = fluid.optimizer.AdamOptimizer(self.critic_lrvalue)
optimizer = fluid.optimizer.AdamOptimizer(
learning_rate=fluid.layers.piecewise_decay(
boundaries=self.boundaries, values=self.critic_lrvalue),
regularization=fluid.regularizer.L2Decay(1e-4))
optimizer.minimize(cost)
return cost
def train(self):
"""train"""
inputs = self.model.create_inputs(mode='train')
reward = layers.cast(inputs['reward'], 'float32')
c_Q = self.model.forward(inputs, output_type='c_Q')
max_Q = self.target_model.forward(inputs, output_type='max_Q')
target_Q = self.get_target_Q(max_Q, reward)
loss = layers.reduce_mean(layers.square_error_cost(c_Q, target_Q))
if self.optimizer == 'Adam':
optimizer = fluid.optimizer.Adam(learning_rate=self.lr, epsilon=1e-4)
elif self.optimizer == 'SGD':
optimizer = fluid.optimizer.SGD(learning_rate=self.lr)
optimizer.minimize(loss)
fetch_dict = OrderedDict()
fetch_dict['loss'] = loss # don't rename 'loss', which will be used in parallel exe in computational task
fetch_dict['c_Q'] = c_Q
fetch_dict['reward'] = reward
return {'fetch_dict': fetch_dict}
def train(self):
"""train"""
inputs = self.model.create_inputs(mode='train')
click_id = layers.cast(inputs['click_id'], 'float32') * self._reward_scale
output_dict = self.model.forward(inputs, output_type='c_Q')
c_Q = output_dict['Q']
target_Q = self.get_target_Q(inputs, click_id)
target_Q.stop_gradient = True
loss = layers.reduce_mean(layers.square_error_cost(c_Q, target_Q))
if self.optimizer == 'Adam':
optimizer = fluid.optimizer.Adam(learning_rate=self.lr, epsilon=1e-4)
elif self.optimizer == 'SGD':
optimizer = fluid.optimizer.SGD(learning_rate=self.lr)
optimizer.minimize(loss)
fetch_dict = OrderedDict()
fetch_dict['loss'] = loss # don't rename 'loss', which will be used in parallel exe in computational task
fetch_dict['c_Q'] = c_Q / self._reward_scale
fetch_dict['click_id'] = click_id / self._reward_scale
return {'fetch_dict': fetch_dict}
def learn(self, obs, label):
pred_output = self.model.policy(obs)
cost = layers.square_error_cost(obs, label)
cost = fluid.layers.reduce_mean(cost)
return cost
