dqn.py 文件源码

def make_dqn_body(input_layer, trainable=True):
    end_points = {}
    net = layers.conv2d(inputs=input_layer,
                        num_outputs=16,
                        kernel_size=[8, 8],
                        stride=[4, 4],
                        activation_fn=tf.nn.relu,
                        padding="same",
                        scope="conv1",
                        trainable=trainable)
    end_points['conv1'] = net
    net = layers.conv2d(inputs=net,
                        num_outputs=32,
                        kernel_size=[4, 4],
                        stride=[2, 2],
                        activation_fn=tf.nn.relu,
                        padding="same",
                        scope="conv2",
                        trainable=trainable)
    end_points['conv2'] = net
    out = layers.flatten(net)
    end_points['conv2_flatten'] = out
    return out, end_points