6. Supervised learning and decision making (Levine) - 3
2023年9月23日 702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
05:17总把“我不行”挂嘴边？王阳明早就说了：世界是个草台班子3月前1019观看
00:27为什么日本人特别推崇王阳明1年前1257观看
09:45如何直面死亡？从哲学智慧中获得勇气7月前1558观看
05:51【访谈】儒学的来源和儒学的意义2022年11月1日2110观看
05:37想不开的时候，就读一读王阳明吧1月前1422观看
03:56王阳明临终前，说过一句惊天动地的话，8个字看出王阳明大彻大悟1年前1777观看
01:14王阳明到底有多厉害？1年前3008观看
04:12我们为什么焦虑？王阳明一句话，从根上解决焦虑12月前3800观看
05:29王阳明：遇事烦躁活得不快乐，不是智慧不够，而是“心力不足”1年前4182观看
15:04纪录片：《王阳明心学》(1) - 22022年11月5日3.5万观看
04:04心学的最后践行，王阳明的两广行，圣人生命最后燃烧在南疆1年前1669观看
02:52傅佩荣：王阳明听过吧？但你不一定听过，两百年后有个人和他多像1年前2486观看
第17/18集 · 13:27[6]--历史观与哲学的魅力 - 2
大学课程2022年10月29日2875观看
05:02千古完人王阳明，他的“阳明心学”，到底能带给我们什么？1年前1862观看
第18/24集 · 12:03儒家、道家美学 - 3大学课程2022年11月1日2237观看
01:31傅佩荣：王阳明的知行合一，为什么难？很多人第一件事就搞错了1年前1019观看