-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathrandom-network-distillation.html
1423 lines (847 loc) · 46.9 KB
/
random-network-distillation.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<html class="theme-next mist use-motion" lang="zh-Hans">
<head><meta name="generator" content="Hexo 3.8.0">
<meta name="google-site-verification" content="zu-9nWphPjrzXV8v514mkHknIz4dNfHlib56-KNAu44">
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1">
<meta name="theme-color" content="#222">
<script src="/lib/pace/pace.min.js?v=1.0.2"></script>
<link href="/lib/pace/pace-theme-flash.min.css?v=1.0.2" rel="stylesheet">
<meta http-equiv="Cache-Control" content="no-transform">
<meta http-equiv="Cache-Control" content="no-siteapp">
<script>
(function(i,s,o,g,r,a,m){i["DaoVoiceObject"]=r;i[r]=i[r]||function(){(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;a.charset="utf-8";m.parentNode.insertBefore(a,m)})(window,document,"script",('https:' == document.location.protocol ? 'https:' : 'http:') + "//widget.daovoice.io/widget/356f1943.js","daovoice")
daovoice('init', {
app_id: "356f1943"
});
daovoice('update');
</script>
<link href="/lib/fancybox/source/jquery.fancybox.css?v=2.1.5" rel="stylesheet" type="text/css">
<link href="/lib/font-awesome/css/font-awesome.min.css?v=4.6.2" rel="stylesheet" type="text/css">
<link href="/css/main.css?v=5.1.4" rel="stylesheet" type="text/css">
<link rel="apple-touch-icon" sizes="180x180" href="/images/apple-touch-icon.png?v=5.1.4">
<link rel="icon" type="image/png" sizes="32x32" href="/images/favicon-32x32png?v=5.1.4">
<link rel="icon" type="image/png" sizes="16x16" href="/images/favicon-16x16.png?v=5.1.4">
<link rel="mask-icon" href="/images/logo.svg?v=5.1.4" color="#222">
<meta name="keywords" content="rl,exploration,">
<link rel="alternate" href="/atom.xml" title="Keavnn'Blog" type="application/atom+xml">
<script>
(function(){
if(''){
if (prompt('请输入文章密码','') !== ''){
alert('密码错误！');
history.back();
}
}
})();
</script>
<meta name="description" content="这篇论文提出了RND，是一种新奇的内在探索机制，主要原理是使预测网络的特征逼近随机网络的特征，以此使得预测网络对不熟悉的状态给出较大的预测误差，由此设定内在奖励。 推荐程度中等： 1个额外的固定、不训练的随机网络 RND在强探索（hard exploration，奖励很稀疏）环境中表现不错">
<meta name="keywords" content="rl,exploration">
<meta property="og:type" content="article">
<meta property="og:title" content="Exploration By Random Network Distillation">
<meta property="og:url" content="http://StepNeverStop.github.io/random-network-distillation.html">
<meta property="og:site_name" content="Keavnn'Blog">
<meta property="og:description" content="这篇论文提出了RND，是一种新奇的内在探索机制，主要原理是使预测网络的特征逼近随机网络的特征，以此使得预测网络对不熟悉的状态给出较大的预测误差，由此设定内在奖励。 推荐程度中等： 1个额外的固定、不训练的随机网络 RND在强探索（hard exploration，奖励很稀疏）环境中表现不错">
<meta property="og:locale" content="zh-Hans">
<meta property="og:image" content="http://stepneverstop.github.io/random-network-distillation/pseudo.png">
<meta property="og:updated_time" content="2020-06-23T13:59:09.263Z">
<meta name="twitter:card" content="summary">
<meta name="twitter:title" content="Exploration By Random Network Distillation">
<meta name="twitter:description" content="这篇论文提出了RND，是一种新奇的内在探索机制，主要原理是使预测网络的特征逼近随机网络的特征，以此使得预测网络对不熟悉的状态给出较大的预测误差，由此设定内在奖励。 推荐程度中等： 1个额外的固定、不训练的随机网络 RND在强探索（hard exploration，奖励很稀疏）环境中表现不错">
<meta name="twitter:image" content="http://stepneverstop.github.io/random-network-distillation/pseudo.png">
<script type="text/javascript" id="hexo.configurations">
var NexT = window.NexT || {};
var CONFIG = {
root: '/',
scheme: 'Mist',
version: '5.1.4',
sidebar: {"position":"left","display":"post","offset":12,"b2t":false,"scrollpercent":true,"onmobile":true},
fancybox: true,
tabs: true,
motion: {"enable":true,"async":true,"transition":{"post_block":"fadeIn","post_header":"slideDownIn","post_body":"slideDownIn","coll_header":"slideLeftIn","sidebar":"slideUpIn"}},
duoshuo: {
userId: '0',
author: '博主'
},
algolia: {
applicationID: '',
apiKey: '',
indexName: '',
hits: {"per_page":10},
labels: {"input_placeholder":"Search for Posts","hits_empty":"We didn't find any results for the search: ${query}","hits_stats":"${hits} results found in ${time} ms"}
}
};
</script>
<link rel="canonical" href="http://StepNeverStop.github.io/random-network-distillation.html">
<title>Exploration By Random Network Distillation | Keavnn'Blog</title>
</head>
<body itemscope="" itemtype="http://schema.org/WebPage" lang="zh-Hans">
<div class="container sidebar-position-left page-post-detail">
<div class="headband"></div>
<a href="https://github.com/StepNeverStop" class="github-corner" aria-label="View source on GitHub" rel="external nofollow" target="_blank"><svg width="80" height="80" viewbox="0 0 250 250" style="fill:#151513; color:#fff; position: absolute; top: 0; border: 0; right: 0;" aria-hidden="true"><path d="M0,0 L115,115 L130,115 L142,142 L250,250 L250,0 Z"/><path d="M128.3,109.0 C113.8,99.7 119.0,89.6 119.0,89.6 C122.0,82.7 120.5,78.6 120.5,78.6 C119.2,72.0 123.4,76.3 123.4,76.3 C127.3,80.9 125.5,87.3 125.5,87.3 C122.9,97.6 130.6,101.9 134.4,103.2" fill="currentColor" style="transform-origin: 130px 106px;" class="octo-arm"/><path d="M115.0,115.0 C114.9,115.1 118.7,116.5 119.8,115.4 L133.7,101.6 C136.9,99.2 139.9,98.4 142.2,98.6 C133.8,88.0 127.5,74.4 143.8,58.0 C148.5,53.4 154.0,51.2 159.7,51.0 C160.3,49.4 163.2,43.6 171.4,40.1 C171.4,40.1 176.1,42.5 178.8,56.2 C183.1,58.6 187.2,61.8 190.9,65.4 C194.5,69.0 197.7,73.2 200.1,77.6 C213.8,80.2 216.3,84.9 216.3,84.9 C212.7,93.1 206.9,96.0 205.4,96.6 C205.1,102.4 203.0,107.8 198.3,112.5 C181.9,128.9 168.3,122.5 157.7,114.1 C157.9,116.9 156.7,120.9 152.7,124.9 L141.0,136.5 C139.8,137.7 141.6,141.9 141.8,141.8 Z" fill="currentColor" class="octo-body"/></svg></a><style>.github-corner:hover .octo-arm{animation:octocat-wave 560ms ease-in-out}@keyframes octocat-wave{0%,100%{transform:rotate(0)}20%,60%{transform:rotate(-25deg)}40%,80%{transform:rotate(10deg)}}@media (max-width:500px){.github-corner:hover .octo-arm{animation:none}.github-corner .octo-arm{animation:octocat-wave 560ms ease-in-out}}</style>
<header id="header" class="header" itemscope="" itemtype="http://schema.org/WPHeader">
<div class="header-inner"><div class="site-brand-wrapper">
<div class="site-meta ">
<div class="custom-logo-site-title">
<a href="/" class="brand" rel="start">
<span class="logo-line-before"><i></i></span>
<span class="site-title">Keavnn'Blog</span>
<span class="logo-line-after"><i></i></span>
</a>
</div>
<h1 class="site-subtitle" itemprop="description">If it is to be, it is up to me.</h1>
</div>
<div class="site-nav-toggle">
<button>
<span class="btn-bar"></span>
<span class="btn-bar"></span>
<span class="btn-bar"></span>
</button>
</div>
</div>
<nav class="site-nav">
<ul id="menu" class="menu">
<li class="menu-item menu-item-home">
<a href="/" rel="section">
<i class="menu-item-icon fa fa-fw fa-home"></i> <br>
首页
</a>
</li>
<li class="menu-item menu-item-about">
<a href="/about/" rel="section">
<i class="menu-item-icon fa fa-fw fa-user"></i> <br>
关于
</a>
</li>
<li class="menu-item menu-item-tags">
<a href="/tags/" rel="section">
<i class="menu-item-icon fa fa-fw fa-tags"></i> <br>
标签
</a>
</li>
<li class="menu-item menu-item-categories">
<a href="/categories/" rel="section">
<i class="menu-item-icon fa fa-fw fa-th"></i> <br>
分类
</a>
</li>
<li class="menu-item menu-item-archives">
<a href="/archives/" rel="section">
<i class="menu-item-icon fa fa-fw fa-archive"></i> <br>
归档
</a>
</li>
<li class="menu-item menu-item-search">
<a href="javascript:;" class="popup-trigger">
<i class="menu-item-icon fa fa-search fa-fw"></i> <br>
搜索
</a>
</li>
</ul>
<div class="site-search">
<div class="popup search-popup local-search-popup">
<div class="local-search-header clearfix">
<span class="search-icon">
<i class="fa fa-search"></i>
</span>
<span class="popup-btn-close">
<i class="fa fa-times-circle"></i>
</span>
<div class="local-search-input-wrapper">
<input autocomplete="off" placeholder="搜索..." spellcheck="false" type="text" id="local-search-input">
</div>
</div>
<div id="local-search-result"></div>
</div>
</div>
</nav>
</div>
</header>
<main id="main" class="main">
<div class="main-inner">
<div class="content-wrap">
<div id="content" class="content">
<div id="posts" class="posts-expand">
<article class="post post-type-normal" itemscope="" itemtype="http://schema.org/Article">
<div class="post-block">
<link itemprop="mainEntityOfPage" href="http://StepNeverStop.github.io/random-network-distillation.html">
<span hidden itemprop="author" itemscope="" itemtype="http://schema.org/Person">
<meta itemprop="name" content="Keavnn">
<meta itemprop="description" content="">
<meta itemprop="image" content="/images/Kicon.jpg">
</span>
<span hidden itemprop="publisher" itemscope="" itemtype="http://schema.org/Organization">
<meta itemprop="name" content="Keavnn'Blog">
</span>
<header class="post-header">
<h2 class="post-title" itemprop="name headline">Exploration By Random Network Distillation</h2>
<div class="post-meta">
<span class="post-time">
<span class="post-meta-item-icon">
<i class="fa fa-calendar-o"></i>
</span>
<span class="post-meta-item-text">发表于</span>
<time title="创建于" itemprop="dateCreated datePublished" datetime="2020-06-23T20:50:48+08:00">
2020-06-23
</time>
<span class="post-meta-divider">|</span>
<span class="post-meta-item-icon">
<i class="fa fa-calendar-check-o"></i>
</span>
<span class="post-meta-item-text">更新于:</span>
<time title="更新于" itemprop="dateModified" datetime="2020-06-23T21:59:09+08:00">
2020-06-23
</time>
</span>
<span class="post-category">
<span class="post-meta-divider">|</span>
<span class="post-meta-item-icon">
<i class="fa fa-folder-o"></i>
</span>
<span class="post-meta-item-text">分类于</span>
<span itemprop="about" itemscope="" itemtype="http://schema.org/Thing">
<a href="/categories/ReinforcementLearning/" itemprop="url" rel="index">
<span itemprop="name">ReinforcementLearning</span>
</a>
</span>
</span>
<div class="post-wordcount">
<span class="post-meta-item-icon">
<i class="fa fa-file-word-o"></i>
</span>
<span class="post-meta-item-text">字数统计:</span>
<span title="字数统计">
1.5k
</span>
<span class="post-meta-divider">|</span>
<span class="post-meta-item-icon">
<i class="fa fa-clock-o"></i>
</span>
<span class="post-meta-item-text">阅读时长 ≈</span>
<span title="阅读时长">
5
</span>
</div>
</div>
</header>
<div class="post-body" itemprop="articleBody">
<p>这篇论文提出了RND，是一种新奇的内在探索机制，主要原理是使预测网络的特征逼近随机网络的特征，以此使得预测网络对不熟悉的状态给出较大的预测误差，由此设定内在奖励。</p>
<p>推荐程度中等：</p>
<ul>
<li>1个额外的固定、不训练的随机网络</li>
<li>RND在强探索（hard exploration，奖励很稀疏）环境中表现不错</li>
</ul>
<a id="more"></a>
<h1 id="简介"><a href="#简介" class="headerlink" title="简介"></a>简介</h1><p>论文地址：<a href="http://arxiv.org/abs/1810.12894" rel="external nofollow" target="_blank">http://arxiv.org/abs/1810.12894</a></p>
<p>代码地址：<a href="https://github.com/openai/random-network-distillation" rel="external nofollow" target="_blank">https://github.com/openai/random-network-distillation</a></p>
<p>这篇论文的思想特别简单：<strong>设定一个固定不训练的随机网络和一个训练的预测网络，将同一个图像特征分别放入两个网络计算出特征，两个特征的差异程度就为状态的“新奇”程度，由此设定内在奖励</strong>。</p>
<p>这么做的意义在与：对于与环境交互时经常访问到的状态，其预测网络产生的特征与随机网络产生的特征相差无几，但是如果突然出现一个新的状态或者很少遇见的状态，那么预测网络与随机网络输出之间的差异性比较大，由此说明状态的新颖性，并根据新颖性计算出内在奖励值。<strong>状态常见，则内在奖励小；状态罕见，则内在奖励大。</strong></p>
<p>这篇论文提出的RND方法在蒙特祖玛的复仇（Montezuma’s Revenge）上刷新了历史新高，并且没有使用专家示例经验（如模仿学习），也没有使用额外的游戏状态信息。</p>
<h1 id="方法"><a href="#方法" class="headerlink" title="方法"></a>方法</h1><p>奖励包括内在与外在两部分：</p>
<script type="math/tex; mode=display">
r_{t}=e_{t}+i_{t}</script><p>其中，$e_t$为外部奖励，即环境给的，$i_t$为内部激励。</p>
<p>传统的内在奖励方法主要分为2类：</p>
<ol>
<li><p>计数</p>
<ol>
<li><p>在tabular setting中，状态、动作都是可数的，可以用一张表格来维护每个状态-动作对的访问次数，并且使用形如以下的公式来定义内在奖励：</p>
<script type="math/tex; mode=display">
i_t=\frac{1}{n_t(s)}</script><script type="math/tex; mode=display">
i_t=\frac{1}{\sqrt {n_t(s)}}</script></li>
<li><p>在非tabular setting中，状态、动作不完全可数，则可使用伪计数法（pseudo-counts），主要是使用状态密度估计的变化程度来衡量。参考论文：《Unifying count-based exploration and intrinsic motivation》</p>
</li>
</ol>
</li>
<li><p>预测，主要是通过前向(s,a->s’)/反向(s,s’->a)预测环境动态的误差来设计内在奖励</p>
</li>
</ol>
<blockquote>
<p>This paper introduces a different approach where the prediction problem is randomly generated. This involves two neural networks: a fixed and randomly initialized target network which sets the prediction problem, and a predictor network trained on data collected by the agent.</p>
</blockquote>
<p>RND引入两个网络：</p>
<ol>
<li>预测网络，是强化学习网络的一部分，可训练，预测观测值的隐特征，$\hat{f}: \mathcal{O} \rightarrow \mathbb{R}^{k}$；</li>
<li>目标网络，额外引入的网络，不训练，固定参数，随机初始化，输出观测值的隐特征作为真值，$f: \mathcal{O} \rightarrow \mathbb{R}^{k}$。</li>
</ol>
<p>目标函数即为最小二乘误差：$|\hat{f}(\mathbf{x} ; \theta)-f(\mathbf{x})|^{2}$，优化参数$\theta_{\hat{f}}$。这个过程将随机初始化的神经网络蒸馏到一个学习的预测网络中去。</p>
<p><strong>上边的最小二乘误差也为内在奖励$i_t$</strong>，作者在使用这个内在奖励时还对其进行了归一化，因为任务不同，这个最小二乘的值偏好范围也不相同，所以作者对这个内在奖励进行了正态分布归一化的处理，主要是将最小二乘误差除以移动平均标准差估计。</p>
<p>作者说这个预测误差主要有以下几个影响因素：</p>
<ol>
<li>训练数据少。当样本中相似数据不多时，预测误差会大</li>
<li>环境的动态随机。环境的随机转换是前向预测误差增大的原因。</li>
<li>模型不适合。关键信息缺失（我也不明白是什么关键信息）或者预测的模型不足以拟合目标函数，会使得预测误差很大</li>
<li>动态学习。学习到最后不能稳定地拟合目标函数。</li>
</ol>
<p>作者还建议在使用内在奖励时，分开两个值函数估计，即一个值函数近似外部奖励，另一个值函数近似内部奖励。这是因为外部奖励稳定，内部奖励不稳定。</p>
<blockquote>
<p>Note that even where one is not trying to combine episodic and non-episodic reward streams, or reward streams with different discount factors, there may still be a benefit to having separate value functions since there is an additional supervisory signal to the value function. This may be especially important for exploration bonuses since the extrinsic reward function is stationary whereas the intrinsic reward function is non-stationary.</p>
</blockquote>
<h2 id="状态归一化"><a href="#状态归一化" class="headerlink" title="状态归一化"></a>状态归一化</h2><p>作者在训练时还对状态进行了归一化处理，主要是以下流程：</p>
<ol>
<li>在训练开始之前与环境随机交互取得经验初始化移动平均均值和标准差</li>
<li>训练开始之后，状态减去移动平均均值</li>
<li>再除以移动平均标准差</li>
<li>将状态clip到[-5, 5]</li>
</ol>
<p>作者对状态归一化的原因主要是不归一化的状态其特征的方差非常低，携带信息少（属实不明白）：</p>
<blockquote>
<p>Observation normalization is often important in deep learning but it is crucial when using a random neural network as a target, since the parameters are frozen and hence cannot adjust to the scale of different datasets. </p>
<p>Lack of normalization can result in the variance of the embedding being extremely low and carrying little information about the inputs.</p>
</blockquote>
<p>这个状态归一化，作者用在随机目标网络和预测网络，但是没有用在策略网络中。</p>
<h1 id="伪代码"><a href="#伪代码" class="headerlink" title="伪代码"></a>伪代码</h1><p><img src="./random-network-distillation/pseudo.png" alt=""></p>
<h1 id="评价"><a href="#评价" class="headerlink" title="评价"></a>评价</h1><p>作者认为RND在局部探索中有用，在全局探索中作用不明显：</p>
<blockquote>
<p>We find that the RND exploration bonus is sufficient to deal with local exploration, i.e. exploring the consequences of short-term decisions, like whether to interact with a particular object, or avoid it. However global exploration that involves coordinated decisions over long time horizons is beyond the reach of our method.</p>
</blockquote>
</div>
<div>
<div>
<div style="text-align:center;color: #ccc;font-size:14px;">-------------本文结束<i class="fa fa-heart"></i>感谢您的阅读-------------</div>
</div>
</div>
<div>
<div class="my_post_copyright">
<script src="//cdn.bootcss.com/clipboard.js/1.5.10/clipboard.min.js"></script>
<!-- JS库 sweetalert 可修改路径 -->
<script src="https://cdn.bootcss.com/jquery/2.0.0/jquery.min.js"></script>
<script src="https://unpkg.com/sweetalert/dist/sweetalert.min.js"></script>
<p><span>本文标题:</span><a href="/random-network-distillation.html">Exploration By Random Network Distillation</a></p>
<p><span>文章作者:</span><a href="/" title="访问 Keavnn 的个人博客">Keavnn</a></p>
<p><span>发布时间:</span>2020年06月23日 - 20:06</p>
<p><span>最后更新:</span>2020年06月23日 - 21:06</p>
<p><span>原始链接:</span><a href="/random-network-distillation.html" title="Exploration By Random Network Distillation">http://StepNeverStop.github.io/random-network-distillation.html</a>
<span class="copy-path" title="点击复制文章链接"><i class="fa fa-clipboard" data-clipboard-text="http://StepNeverStop.github.io/random-network-distillation.html" aria-label="复制成功！"></i></span>
</p>
<p><span>许可协议:</span><i class="fa fa-creative-commons"></i> <a rel="external nofollow" href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank" title="Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)">署名-非商业性使用-相同方式共享 4.0 国际</a> 转载请保留原文链接及作者。</p>
</div>
<script>
var clipboard = new Clipboard('.fa-clipboard');
$(".fa-clipboard").click(function(){
clipboard.on('success', function(){
swal({
title: "",
text: '复制成功',
icon: "success",
showConfirmButton: true
});
});
});
</script>
</div>
<div>
<div style="padding: 10px 0; margin: 20px auto; width: 90%; text-align: center;">
<div>如果您获得了帮助，也可以资助一下小的啦~</div>
<button id="rewardButton" disable="enable" onclick="var qr = document.getElementById('QR'); if (qr.style.display === 'none') {qr.style.display='block';} else {qr.style.display='none'}">
<span>打赏啦</span>
</button>
<div id="QR" style="display: none;">
<div id="wechat" style="display: inline-block">
<img id="wechat_qr" src="/images/wechatpay.jpg" alt="Keavnn 微信">
<p>微信</p>
</div>
<div id="alipay" style="display: inline-block">
<img id="alipay_qr" src="/images/alipay.jpg" alt="Keavnn 支付宝">
<p>支付宝</p>
</div>
</div>
</div>
</div>
<footer class="post-footer">
<div class="post-tags">
<a href="/tags/rl/" rel="tag"> <i class="fa fa-tag"></i> rl</a>
<a href="/tags/exploration/" rel="tag"> <i class="fa fa-tag"></i> exploration</a>
</div>
<div class="post-nav">
<div class="post-nav-next post-nav-item">
<a href="/rl-code-pit.html" rel="next" title="在DRL路上踩过的坑">
<i class="fa fa-chevron-left"></i> 在DRL路上踩过的坑
</a>
</div>
<span class="post-nav-divider"></span>
<div class="post-nav-prev post-nav-item">
</div>
</div>
</footer>
</div>
</article>
<div class="post-spread">
<!-- Go to www.addthis.com/dashboard to customize your tools -->
<div class="addthis_inline_share_toolbox">
<script type="text/javascript" src="//s7.addthis.com/js/300/addthis_widget.js#pubid=ra-5cefbfc88c13b0e7" async="async"></script>
</div>
</div>
</div>
</div>
<div class="comments" id="comments">
<div id="lv-container" data-id="city" data-uid="MTAyMC80MTk0NS8xODQ5MQ=="></div>
</div>
</div>
<div class="sidebar-toggle">
<div class="sidebar-toggle-line-wrap">
<span class="sidebar-toggle-line sidebar-toggle-line-first"></span>
<span class="sidebar-toggle-line sidebar-toggle-line-middle"></span>
<span class="sidebar-toggle-line sidebar-toggle-line-last"></span>
</div>
</div>
<aside id="sidebar" class="sidebar">
<div id="sidebar-dimmer"></div>
<div class="sidebar-inner">
<ul class="sidebar-nav motion-element">
<li class="sidebar-nav-toc sidebar-nav-active" data-target="post-toc-wrap">
文章目录
</li>
<li class="sidebar-nav-overview" data-target="site-overview-wrap">
站点概览
</li>
</ul>
<section class="site-overview-wrap sidebar-panel">
<div class="site-overview">
<div class="site-author motion-element" itemprop="author" itemscope="" itemtype="http://schema.org/Person">
<img class="site-author-image" itemprop="image" src="/images/Kicon.jpg" alt="Keavnn">
<p class="site-author-name" itemprop="name">Keavnn</p>
<p class="site-description motion-element" itemprop="description">If it is to be, it is up to me.</p>
</div>
<nav class="site-state motion-element">
<div class="site-state-item site-state-posts">
<a href="/archives/">
<span class="site-state-item-count">51</span>
<span class="site-state-item-name">日志</span>
</a>
</div>
<div class="site-state-item site-state-categories">
<a href="/categories/index.html">
<span class="site-state-item-count">11</span>
<span class="site-state-item-name">分类</span>
</a>
</div>
<div class="site-state-item site-state-tags">
<a href="/tags/index.html">
<span class="site-state-item-count">26</span>
<span class="site-state-item-name">标签</span>
</a>
</div>
</nav>
<div class="feed-link motion-element">
<a href="/atom.xml" rel="alternate">
<i class="fa fa-rss"></i>
RSS
</a>
</div>
<div class="links-of-author motion-element">
<span class="links-of-author-item">
<a href="https://github.com/StepNeverStop" target="_blank" title="GitHub" rel="external nofollow">
<i class="fa fa-fw fa-github"></i>GitHub</a>
</span>
<span class="links-of-author-item">
<a href="mailto:[email protected]" target="_blank" title="E-Mail" rel="external nofollow">
<i class="fa fa-fw fa-envelope"></i>E-Mail</a>
</span>
</div>
<div class="cc-license motion-element" itemprop="license">
<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" class="cc-opacity" target="_blank" rel="external nofollow">
<img src="/images/cc-by-nc-sa.svg" alt="Creative Commons">
</a>
</div>
<div class="links-of-blogroll motion-element links-of-blogroll-inline">
<div class="links-of-blogroll-title">
<i class="fa fa-fw fa-link"></i>
推荐阅读
</div>
<ul class="links-of-blogroll-list">
<li class="links-of-blogroll-item">
<a href="https://bluefisher.github.io" title="Fisher Chang" target="_blank" rel="external nofollow">Fisher Chang</a>
</li>
</ul>
</div>
</div>
</section>
<!--noindex-->
<section class="post-toc-wrap motion-element sidebar-panel sidebar-panel-active">
<div class="post-toc">
<div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-1"><a class="nav-link" href="#简介"><span class="nav-number">1.</span> <span class="nav-text">简介</span></a></li><li class="nav-item nav-level-1"><a class="nav-link" href="#方法"><span class="nav-number">2.</span> <span class="nav-text">方法</span></a><ol class="nav-child"><li class="nav-item nav-level-2"><a class="nav-link" href="#状态归一化"><span class="nav-number">2.1.</span> <span class="nav-text">状态归一化</span></a></li></ol></li><li class="nav-item nav-level-1"><a class="nav-link" href="#伪代码"><span class="nav-number">3.</span> <span class="nav-text">伪代码</span></a></li><li class="nav-item nav-level-1"><a class="nav-link" href="#评价"><span class="nav-number">4.</span> <span class="nav-text">评价</span></a></li></ol></div>
</div>
</section>
<!--/noindex-->
</div>
</aside>
</div>
</main>
<footer id="footer" class="footer">
<div class="footer-inner">
<script async src="https://busuanzi.ibruce.info/busuanzi/2.3/busuanzi.pure.mini.js"></script>
<div class="copyright">© <span itemprop="copyrightYear">2020</span>
<span class="with-love">
<i class="fa fa-heart"></i>
</span>
<span class="author" itemprop="copyrightHolder">Keavnn</span>
<span class="post-meta-divider">|</span>
<span class="post-meta-item-icon">
<i class="fa fa-area-chart"></i>
</span>
<span class="post-meta-item-text">Site words total count:</span>
<span title="Site words total count">80.3k</span>
</div>
<div class="powered-by">
<i class="fa fa-user-md"></i><span id="busuanzi_container_site_pv">
本站总访问量<span id="busuanzi_value_site_pv"></span>次
</span>
</div>
<!-- <div class="theme-info">
<div class="powered-by"></div>
<span class="post-count">博客全站共80.3k字</span>
</div> -->
</div>
</footer>
<div class="back-to-top">
<i class="fa fa-arrow-up"></i>
<span id="scrollpercent"><span>0</span>%</span>
</div>
</div>
<script type="text/javascript">
if (Object.prototype.toString.call(window.Promise) !== '[object Function]') {
window.Promise = null;
}
</script>
<script type="text/javascript" src="/lib/jquery/index.js?v=2.1.3"></script>
<script type="text/javascript" src="/lib/fastclick/lib/fastclick.min.js?v=1.0.6"></script>
<script type="text/javascript" src="/lib/jquery_lazyload/jquery.lazyload.js?v=1.9.7"></script>
<script type="text/javascript" src="/lib/velocity/velocity.min.js?v=1.2.1"></script>
<script type="text/javascript" src="/lib/velocity/velocity.ui.min.js?v=1.2.1"></script>
<script type="text/javascript" src="/lib/fancybox/source/jquery.fancybox.pack.js?v=2.1.5"></script>
<script type="text/javascript" src="/js/src/utils.js?v=5.1.4"></script>
<script type="text/javascript" src="/js/src/motion.js?v=5.1.4"></script>
<script type="text/javascript" src="/js/src/scrollspy.js?v=5.1.4"></script>
<script type="text/javascript" src="/js/src/post-details.js?v=5.1.4"></script>
<script type="text/javascript" src="/js/src/bootstrap.js?v=5.1.4"></script>