49. Transfer in Reinforcement Learning (Finn) - 1
2023年9月23日 1381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74/81集 · 08:12(1)--12.1情商修炼在于细节 - 1
大学课程2022年10月27日3085观看
27:41让你成为人际关系题的老司机 - 22022年10月27日2433观看
00:50关于人脉，你可能理解错了！4月前1491观看
00:19高手都是这样处理人际关系的！4月前1074观看- 11:52第三部分 第六讲 人际关系 第二节 - 32022年10月31日1602观看
26:28如何提升情商，搞定人际关系，一条视频讲清楚9月前3319观看
02:23怎么委婉拒绝女生，还能保持良好人际关系？10月前1513观看
第9/41集 · 07:53融洽关系与反思性倾听 - 1大学课程2022年10月13日1513观看
08:11[1]--人际沟通题 - 12022年10月27日1833观看
11:53如何用博弈论搞定人际关系10月前1060观看
26:43【十天干详解】甲木的基本属性、典型优势、潜在弱点、亲密关系、人际关系、原生家庭、身体健康1月前862观看
00:32一定要告诉孩子，这些交友原则7月前1191观看
00:56个教养细节，让孩子走哪儿都有贵人缘7月前848观看
01:59情绪三阶段 #干货分享 #人际交往 #自我提升 #智慧人生 #知识领航者1年前2586观看
第10/22集 · 09:02Day16-长期个案目标：重塑情感认知，将自律和高情商注入客户心智 - 3大学课程2022年9月29日3086观看
01:56新学期，孩子的人际关系真的重要吗？2023年10月9日1518观看