deep_q.py 文件源码

def _build_model(self):
        # Neural Net for Deep-Q learning Model
        model = Sequential()
        #model.add(Conv2D(256, kernel_size = (2,2), activation='relu', input_shape=(self.state_size.shape[0], self.state_size.shape[1],1), padding="same"))
        #model.add(Conv2D(712, kernel_size = (2,2), activation='relu', padding="same"))
        #model.add(Conv2D(128, kernel_size = (2,2), activation='relu', padding="same"))
        model.add(Dense(2048, input_dim=5, activation='relu'))#self.state_size.shape[0] * self.state_size.shape[1]
        #model.add(Flatten())
        model.add(Dense(1024, activation='relu'))
        model.add(Dropout(0.5))
        model.add(Dense(512, activation='relu'))
        model.add(Dense(256, activation='relu'))
        model.add(Dropout(0.5))
        model.add(Dense(128, activation='relu'))
        model.add(Dense(64, activation='relu'))
        model.add(Dropout(0.5))
        model.add(Dense(32, activation='relu'))
        model.add(Dense(16, activation='relu'))
        model.add(Dropout(0.5))
        model.add(Dense(8, activation='relu'))
        model.add(Dense(4, activation='linear'))
        model.compile(loss='mse',
                      optimizer=Adam(lr=self.learning_rate))
        return model