29. Advanced topics in imitation and safety (Finn) - 2
2年前 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
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