train_policy_gradient.py 文件源码

def build_model(self):
        model = Sequential()
        model.add(Dense(self.hidden1, input_dim=self.state_size, activation='relu', kernel_initializer='glorot_uniform'))
        model.add(Dense(self.hidden2, activation='relu', kernel_initializer='glorot_uniform'))
        model.add(Dense(self.action_size, activation='softmax', kernel_initializer='glorot_uniform'))
        model.summary()
        # Using categorical crossentropy as a loss is a trick to easily
        # implement the policy gradient. Categorical cross entropy is defined
        # H(p, q) = sum(p_i * log(q_i)). For the action taken, a, you set
        # p_a = advantage. q_a is the output of the policy network, which is
        # the probability of taking the action a, i.e. policy(s, a).
        # All other p_i are zero, thus we have H(p, q) = A * log(policy(s, a))
        model.compile(loss="categorical_crossentropy", optimizer=Adam(lr=self.learning_rate))
        return model

    # using the output of policy network, pick action stochastically