强化学习 ---baselines项目之 Atari游戏的网络结构解析

这个baselines项目设计的比较灵活，结构有点复杂。由于项目庞大，各个函数之间又是相互调用，有时候从一个函数追溯下去，可以追溯6,7层，每个函数的超参数又特别多，很容易把人搞晕。
接下来只看DQN部分的源码，其他无关的先不看，沿着一条线分解它！
接下来进行一个递归游戏，一层一层的深入探索。
在deepq下有个experiments，这下面全实例，pong就是一个Atari游戏的实验。
在这里插入图片描述
1.

from baselines import deepq
from baselines import bench
from baselines import logger
from baselines.common.atari_wrappers import make_atari
import numpy as np
np.seterr(invalid='ignore')

def main():
    logger.configure()
    env = make_atari('PongNoFrameskip-v4')
    env = bench.Monitor(env, logger.get_dir())
    env = deepq.wrap_atari_dqn(env)
    model = deepq.learn(
        env,
        "conv_only",
        convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
        hiddens=[256],
        dueling=True,
        lr=1e-4,
        total_timesteps=int(1e7),
        buffer_size=10000,
        exploration_fraction=0.1,
        exploration_final_eps=0.01,
        train_freq=4,
        learning_starts=10000,
        target_network_update_freq=1000,
        gamma=0.99,
    )
    deepq.learn()

    model.save('pong_model.pkl')
    env.close()

if __name__ == '__main__':
    main()

以上代码主要调用了deepq文件。并且仔细看下deepq.learn()方法，参数很多，我们先关注它的网络结构，明显卷积层conv和全连接层hiddens是属于网络结构部分。按住ctrl点击learn（）方法，会跳转到learn（）方法位置。以下是learn（）的超参数。别怕，没那么复杂，别人的工程写的还是挺容易读懂的。
2.

def learn(env,
          network,
          seed=None,
          lr=5e-4,
          total_timesteps=100000,
          buffer_size=50000,
          exploration_fraction=0.1,
          exploration_final_eps=0.02,
          train_freq=1,
          batch_size=32,
          print_freq=100,
          checkpoint_freq=10000,
          checkpoint_path=None,
          learning_starts=1000,
          gamma=1.0,
          target_network_update_freq=500,
          prioritized_replay=False,
          prioritized_replay_alpha=0.6,
          prioritized_replay_beta0=0.4,
          prioritized_replay_beta_iters=None,
          prioritized_replay_eps=1e-6,
          param_noise=False,
          callback=None,
          load_path=None,
          **network_kwargs
          ):

可是这里面没有出现参数convs等，那就说明这些参数是在 **network_kwargs（一个字典）里面经过仔细对比可知：

**network_kwargs里有{convs， hiddens，dueling}这3个
再看以下哪个函数用到了 **network_kwargs
3.

q_func = build_q_func(network, **network_kwargs)

只有这一条用到了，nice！
点进去
4.

def build_q_func(network, hiddens=[256], dueling=True, layer_norm=False, **network_kwargs):
    if isinstance(network, str):
        from baselines.common.models import get_network_builder
       # print('network:',network)
        network = get_network_builder(network)(**network_kwargs)
       # print('network:', network)
    def q_func_builder(input_placeholder, num_actions, scope, reuse=False):
        with tf.variable_scope(scope, reuse=reuse):
            latent = network(input_placeholder)
            if isinstance(latent, tuple):
                if latent[1] is not None:
                    raise NotImplementedError("DQN is not compatible with recurrent policies yet")
                latent = latent[0]

            latent = layers.flatten(latent)

            with tf.variable_scope("action_value"):
                action_out = latent
                for hidden in hiddens:
                    action_out = layers.fully_connected(action_out, num_outputs=hidden, activation_fn=None)
                    if layer_norm:
                        action_out = layers.layer_norm(action_out, center=True, scale=True)
                    action_out = tf.nn.relu(action_out)
                action_scores = layers.fully_connected(action_out, num_outputs=num_actions, activation_fn=None)

            if dueling:
                with tf.variable_scope("state_value"):
                    state_out = latent
                    for hidden in hiddens:
                        state_out = layers.fully_connected(state_out, num_outputs=hidden, activation_fn=None)
                        if layer_norm:
                            state_out = layers.layer_norm(state_out, center=True, scale=True)
                        state_out = tf.nn.relu(state_out)
                    state_score = layers.fully_connected(state_out, num_outputs=1, activation_fn=None)
                action_scores_mean = tf.reduce_mean(action_scores, 1)
                action_scores_centered = action_scores - tf.expand_dims(action_scores_mean, 1)
                q_out = state_score + action_scores_centered
            else:
                q_out = action_scores
            return q_out

    return q_func_builder

很接近了，这个明显就是在构建网络结构了， **network_kwargs里面使用了两个参数，还剩下‘conv’ 没用，看3可以知道它返回的是一个函数，也就是这里面的q_func_builder，看这个函数前，还要进入下一层探索，先看下开头的这行
5.

network = get_network_builder(network)(**network_kwargs)

从刚刚的传参我们知道network = “conv_only” ，是一个字符串。通过一个字符串，返回了一个函数，并且再把**network_kwargs（也就是卷积层的参数）参数传递给返回的这个函数，目测最终返回一个卷积层的网络结构。先点进去看一下：
6.

def get_network_builder(name):

    if callable(name):  #函数用于检查一个对象是否是可调用的
        # print('name',name)
        return name
    elif name in mapping:
       # print('mapping',mapping)
        return mapping[name]

    else:
        raise ValueError('Unknown network type: {}'.format(name))

这段代码很简洁：先检测下name是否是可调用的参数，再检测是否在mapping字典里，然后返回函数名。显然这里的“conv_only”是从mapping里面去取。
我们把mappling输出来看一下
7.

print(mapping)
output:
mapping {'mlp': <function mlp at 0x000001C90316B5E8>, 'cnn': <function cnn at 0x000001C90316B678>, 'impala_cnn': <function impala_cnn at 0x000001C90316B708>, 'cnn_small': <function cnn_small at 0x000001C90316B798>, 'lstm': <function lstm at 0x000001C90316B828>, 'cnn_lstm': <function cnn_lstm at 0x000001C90316B8B8>, 'impala_cnn_lstm': <function impala_cnn_lstm at 0x000001C90316B948>, 'cnn_lnlstm': <function cnn_lnlstm at 0x000001C90316B9D8>, 'conv_only': <function conv_only at 0x000001C90316BA68>}
network: <function conv_only.<locals>.network_fn at 0x000001C9030AF678>

发现有那么多函数，那么这些函数是什么时候加入mapping的呢？
在该文件搜索下mapping，发现register（）函数：
8.

mapping = {}

def register(name):
    def _thunk(func):
        mapping[name] = func
        return func
    return _thunk

就是简单的将name和函数名存入mapping
再搜索register，发现了很多register的注解
9.

@register("mlp")
def mlp(num_layers=2, num_hidden=64, activation=tf.tanh, layer_norm=False):...

@register("cnn")
def cnn(**conv_kwargs):...

@register("conv_only")
def conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs):....

其实加上@register(“xxx”)后就会调用上面8函数直接加入mapping，这就正好跟mapping里的函数对应上了。注解是个好东西
到此为止，我也不知道跳了多少层了。只要脑袋里的线路清晰，就不会混乱。那么直接关注conv_only函数吧
10.

@register("conv_only")
def conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs):
    def network_fn(X):
        out = tf.cast(X, tf.float32) / 255.
        with tf.variable_scope("convnet"):
            for num_outputs, kernel_size, stride in convs:
                out = tf.contrib.layers.convolution2d(out,
                                           num_outputs=num_outputs,
                                           kernel_size=kernel_size,
                                           stride=stride,
                                           activation_fn=tf.nn.relu,
                                           **conv_kwargs)

        return out
    return network_fn

先看下整体结构，果然没错，跟我们在5的时候说的一样之前传入的**network_kwargs就变成这个函数的convs了。返回了一个network_fn函数，再传入图像的输入参数----------就是这里的X参数。现在仔细看下这个函数！

convs是一个列表，列表里是一个元组。X是输入的图像参数，是一个shape=[batch,84,84,4]的Tensor。
先量化成0-1的浮点数，再遍历convs里面的元素，搭建卷积网络
num_outputs是通道数，也就是卷积核个数， kernel_size卷积核大小, stride 是上下的跨度大小这些是卷积网络里常用的
输出out是一个【batch，size，size，64】的Tensor

接下来返回4在来看
4.

def build_q_func(network, hiddens=[256], dueling=True, layer_norm=False, **network_kwargs):
    if isinstance(network, str):
        from baselines.common.models import get_network_builder
       # print('network:',network)
        network = get_network_builder(network)(**network_kwargs)
       # print('network:', network)
    def q_func_builder(input_placeholder, num_actions, scope, reuse=False):
        with tf.variable_scope(scope, reuse=reuse):
            latent = network(input_placeholder)
            if isinstance(latent, tuple):
                if latent[1] is not None:
                    raise NotImplementedError("DQN is not compatible with recurrent policies yet")
                latent = latent[0]

            latent = layers.flatten(latent)

            with tf.variable_scope("action_value"):
                action_out = latent
                for hidden in hiddens:
                    action_out = layers.fully_connected(action_out, num_outputs=hidden, activation_fn=None)
                    if layer_norm:
                        action_out = layers.layer_norm(action_out, center=True, scale=True)
                    action_out = tf.nn.relu(action_out)
                action_scores = layers.fully_connected(action_out, num_outputs=num_actions, activation_fn=None)

            if dueling:
                with tf.variable_scope("state_value"):
                    state_out = latent
                    for hidden in hiddens:
                        state_out = layers.fully_connected(state_out, num_outputs=hidden, activation_fn=None)
                        if layer_norm:
                            state_out = layers.layer_norm(state_out, center=True, scale=True)
                        state_out = tf.nn.relu(state_out)
                    state_score = layers.fully_connected(state_out, num_outputs=1, activation_fn=None)
                action_scores_mean = tf.reduce_mean(action_scores, 1)
                action_scores_centered = action_scores - tf.expand_dims(action_scores_mean, 1)
                q_out = state_score + action_scores_centered
            else:
                q_out = action_scores
            return q_out

    return q_func_builder

根据上面的分析，在执行network = get_network_builder(network)(**network_kwargs)语句后，network相当于是10里的network_fn(X)函数

接着分析q_func_builder（）：

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强化学习 ---baselines项目之 Atari游戏的网络结构解析

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