29. Advanced topics in imitation and safety (Finn) - 2
2023年9月23日 1371观看
加州大学伯克利分校 2017 深度增强学习课程
大学课程 / 社会学
https://www.youtube.com/playlist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX CS294-112 Deep Reinforcement Learning Sp17 课程主页：http://rll.berkeley.edu/deeprlcourse/
共57集 7.3万人观看
1Introduction and course overview (Levine, Finn, Schulman) - 126:11- 2Introduction and course overview (Levine, Finn, Schulman) - 226:14
- 3Introduction and course overview (Levine, Finn, Schulman) - 326:08
4Supervised learning and decision making (Levine) - 124:06- 5Supervised learning and decision making (Levine) - 224:07
- 6Supervised learning and decision making (Levine) - 324:03
7Optimal control and planning (Levine) - 121:06- 8Optimal control and planning (Levine) - 221:13
- 9Optimal control and planning (Levine) - 321:03
10Learning dynamical system models from data (Levine) - 127:27- 11Learning dynamical system models from data (Levine) - 227:35
- 12Learning dynamical system models from data (Levine) - 327:22
13Learning policies by imitating optimal controllers (Levine) - 123:05- 14Learning policies by imitating optimal controllers (Levine) - 223:08
- 15Learning policies by imitating optimal controllers (Levine) - 322:58
16RL definitions, value iteration, policy iteration (Schulman) - 117:19- 17RL definitions, value iteration, policy iteration (Schulman) - 217:22
- 18RL definitions, value iteration, policy iteration (Schulman) - 317:18
19Reinforcement learning with policy gradients (Schulman) - 121:48- 20Reinforcement learning with policy gradients (Schulman) - 221:54
- 21Reinforcement learning with policy gradients (Schulman) - 321:42
22Learning Q-functions: Q-learning, SARSA, and others (Schulman) - 125:50- 23Learning Q-functions: Q-learning, SARSA, and others (Schulman) - 225:53
- 24Learning Q-functions: Q-learning, SARSA, and others (Schulman) - 325:42
25Advanced Q-learning: replay buffers, target networks, double Q-learning (Sc - 126:47- 26Advanced Q-learning: replay buffers, target networks, double Q-learning (Sc - 226:55
- 27Advanced Q-learning: replay buffers, target networks, double Q-learning (Sc - 326:41
28Advanced topics in imitation and safety (Finn) - 127:53- 29Advanced topics in imitation and safety (Finn) - 227:56
- 30Advanced topics in imitation and safety (Finn) - 327:47
31Inverse RL: acquiring objectives from demonstration (Finn) - 124:47- 32Inverse RL: acquiring objectives from demonstration (Finn) - 224:48
- 33Inverse RL: acquiring objectives from demonstration (Finn) - 324:47
34Advanced policy gradients: natural gradient and TRPO (Schulman) - 128:05- 35Advanced policy gradients: natural gradient and TRPO (Schulman) - 228:08
- 36Advanced policy gradients: natural gradient and TRPO (Schulman) - 328:02
37Policy gradient variance reduction and actor-critic algorithms (Schulman) - 126:55- 38Policy gradient variance reduction and actor-critic algorithms (Schulman) - 227:00
- 39Policy gradient variance reduction and actor-critic algorithms (Schulman) - 326:51
40Summary of policy gradients and temporal difference methods (Schulman) - 124:06- 41Summary of policy gradients and temporal difference methods (Schulman) - 224:10
- 42Summary of policy gradients and temporal difference methods (Schulman) - 323:59
43The exploration problem (Schulman) - 127:18- 44The exploration problem (Schulman) - 227:18
- 45The exploration problem (Schulman) - 327:17
46Parallel RL algorithms, open problems and challenges in deep reinforcement - 126:14- 47Parallel RL algorithms, open problems and challenges in deep reinforcement - 226:22
- 48Parallel RL algorithms, open problems and challenges in deep reinforcement - 326:11
49Transfer in Reinforcement Learning (Finn) - 128:18- 50Transfer in Reinforcement Learning (Finn) - 228:18
- 51Transfer in Reinforcement Learning (Finn) - 328:16
52Neural Architecture Search with Reinforcement Learning: Quoc Le and Barret Z - 125:24- 53Neural Architecture Search with Reinforcement Learning: Quoc Le and Barret Z - 225:29
- 54Neural Architecture Search with Reinforcement Learning: Quoc Le and Barret Z - 325:17
55Generalization and Safety in Reinforcement Learning and Control: Aviv Tamar - 125:39- 56Generalization and Safety in Reinforcement Learning and Control: Aviv Tamar - 225:40
- 57Generalization and Safety in Reinforcement Learning and Control: Aviv Tamar - 325:33
相关视频
第7/16集 · 18:01、古希腊哲学的辉煌时代(一) - 3
大学课程2022年9月29日4529观看
01:25想读哲学，但实在读不下去？没关系，从这本小书读起10月前1953观看
15:31生活处处有哲学 - 32022年9月29日3908观看
第44/118集 · 09:53分析哲学的诞生（三） - 3大学课程2022年11月18日2002观看
08:08哲学与人生10：伊比鸠鲁的哲学核心是什么2023年6月26日2747观看
01:37每一个人都需要哲学，活得更加清醒4月前1055观看
02:33这本书告诉你人生为什么不能缺少哲学11月前6256观看
第50/119集 · 13:08台湾大学公开课：苑舉正教授讲述怀疑论，思考人生终极目标—24怀疑学派：阿克西劳斯 - 3大学课程2022年11月26日2308观看
01:35傅佩荣：哲学千万别这样读，不是害自己吗？一般人读这三本就够了1年前4681观看
08:02科学的尽头是哲学，哲学的尽头是神学吗？如何区分这三者的本质？2年前2.2万观看
00:53人生的三个阶段：文学、经济学、哲学2月前722观看
00:58傅佩荣：我被这本书吓一跳，因为它教会我的，比整部哲学史还深刻1年前2663观看
02:09傅佩荣：哲学哪有什么诺贝尔奖？7月前1061观看
08:14齐泽克哲学系列第1期：意识形态的本质是什么？5月前1651观看
08:18模块一-第一节-马克思主义哲学(2)(二) - 32022年9月23日1470观看
02:40哲学开始于惊疑。惊疑这个词，在希腊文里包含两重意思，一是惊奇，二是疑惑。惊奇和疑惑其实是两种很不同的...2023年10月7日4446观看