加州大学伯克利分校 2017：深度增强学习课程-Reinforcement learning with policy gradients (Schulman)（上）-网易公开课

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Reinforcement learning with policy gradients (Schulman)（上）

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选集(57)

自动播放

[1] Introduction and ...

5274播放

26:11

Introduction and course overview (Levine, Finn, Schulman)（上）

[2] Introduction and ...

1591播放

26:14

Introduction and course overview (Levine, Finn, Schulman)（中）

[3] Introduction and ...

1293播放

26:08

Introduction and course overview (Levine, Finn, Schulman)（下）

[4] Supervised learni...

1873播放

24:06

Supervised learning and decision making (Levine)（上）

[5] Supervised learni...

1242播放

24:07

Supervised learning and decision making (Levine)（中）

[6] Supervised learni...

702播放

24:03

Supervised learning and decision making (Levine)（下）

[7] Optimal control a...

1595播放

21:06

Optimal control and planning (Levine)（上）

[8] Optimal control a...

617播放

21:13

Optimal control and planning (Levine)（中）

[9] Optimal control a...

536播放

21:03

Optimal control and planning (Levine)（下）

[10] Learning dynamica...

1204播放

27:27

Learning dynamical system models from data (Levine)（上）

[11] Learning dynamica...

1406播放

27:35

Learning dynamical system models from data (Levine)（中）

[12] Learning dynamica...

790播放

27:22

Learning dynamical system models from data (Levine)（下）

[13] Learning policies...

598播放

23:05

Learning policies by imitating optimal controllers (Levine)（上）

[14] Learning policies...

1451播放

23:08

Learning policies by imitating optimal controllers (Levine)（中）

[15] Learning policies...

1175播放

22:58

Learning policies by imitating optimal controllers (Levine)（下）

[16] RL definitions, v...

1155播放

17:19

RL definitions, value iteration, policy iteration (Schulman)（上）

[17] RL definitions, v...

1311播放

17:22

RL definitions, value iteration, policy iteration (Schulman)（中）

[18] RL definitions, v...

517播放

17:18

RL definitions, value iteration, policy iteration (Schulman)（下）

[19] Reinforcement lea...

927播放

待播放

Reinforcement learning with policy gradients (Schulman)（上）

[20] Reinforcement lea...

890播放

21:54

Reinforcement learning with policy gradients (Schulman)（中）

[21] Reinforcement lea...

860播放

21:42

Reinforcement learning with policy gradients (Schulman)（下）

[22] Learning Q-functi...

1353播放

25:50

Learning Q-functions: Q-learning, SARSA, and others (Schulman)（上）

[23] Learning Q-functi...

1128播放

25:53

Learning Q-functions: Q-learning, SARSA, and others (Schulman)（中）

[24] Learning Q-functi...

830播放

25:42

Learning Q-functions: Q-learning, SARSA, and others (Schulman)（下）

[25] Advanced Q-learni...

641播放

26:47

Advanced Q-learning: replay buffers, target networks, double Q-learning (Sc（上）

[26] Advanced Q-learni...

854播放

26:55

Advanced Q-learning: replay buffers, target networks, double Q-learning (Sc（中）

[27] Advanced Q-learni...

1029播放

26:41

Advanced Q-learning: replay buffers, target networks, double Q-learning (Sc（下）

[28] Advanced topics i...

1480播放

27:53

Advanced topics in imitation and safety (Finn)（上）

[29] Advanced topics i...

1371播放

27:56

Advanced topics in imitation and safety (Finn)（中）

[30] Advanced topics i...

1002播放

27:47

Advanced topics in imitation and safety (Finn)（下）

[31] Inverse RL: acqui...

1459播放

24:47

Inverse RL: acquiring objectives from demonstration (Finn)（上）

[32] Inverse RL: acqui...

1195播放

24:48

Inverse RL: acquiring objectives from demonstration (Finn)（中）

[33] Inverse RL: acqui...

1225播放

24:47

Inverse RL: acquiring objectives from demonstration (Finn)（下）

[34] Advanced policy g...

1228播放

28:05

Advanced policy gradients: natural gradient and TRPO (Schulman)（上）

[35] Advanced policy g...

755播放

28:08

Advanced policy gradients: natural gradient and TRPO (Schulman)（中）

[36] Advanced policy g...

917播放

28:02

Advanced policy gradients: natural gradient and TRPO (Schulman)（下）

[37] Policy gradient v...

1186播放

26:55

Policy gradient variance reduction and actor-critic algorithms (Schulman)（上）

[38] Policy gradient v...

1417播放

27:00

Policy gradient variance reduction and actor-critic algorithms (Schulman)（中）

[39] Policy gradient v...

911播放

26:51

Policy gradient variance reduction and actor-critic algorithms (Schulman)（下）

[40] Summary of policy...

920播放

24:06

Summary of policy gradients and temporal difference methods (Schulman)（上）

[41] Summary of policy...

1446播放

24:10

Summary of policy gradients and temporal difference methods (Schulman)（中）

[42] Summary of policy...

1022播放

23:59

Summary of policy gradients and temporal difference methods (Schulman)（下）

[43] The exploration p...

678播放

27:18

The exploration problem (Schulman)（上）

[44] The exploration p...

894播放

27:18

The exploration problem (Schulman)（中）

[45] The exploration p...

727播放

27:17

The exploration problem (Schulman)（下）

[46] Parallel RL algor...

1485播放

26:14

Parallel RL algorithms, open problems and challenges in deep reinforcement（上）

[47] Parallel RL algor...

806播放

26:22

Parallel RL algorithms, open problems and challenges in deep reinforcement（中）

[48] Parallel RL algor...

1507播放

26:11

Parallel RL algorithms, open problems and challenges in deep reinforcement（下）

[49] Transfer in Reinf...

1381播放

28:18

Transfer in Reinforcement Learning (Finn)（上）

[50] Transfer in Reinf...

634播放

28:18

Transfer in Reinforcement Learning (Finn)（中）

[51] Transfer in Reinf...

984播放

28:16

Transfer in Reinforcement Learning (Finn)（下）

[52] Neural Architectu...

1011播放

25:24

Neural Architecture Search with Reinforcement Learning: Quoc Le and Barret Z（上）

[53] Neural Architectu...

934播放

25:29

Neural Architecture Search with Reinforcement Learning: Quoc Le and Barret Z（中）

[54] Neural Architectu...

956播放

25:17

Neural Architecture Search with Reinforcement Learning: Quoc Le and Barret Z（下）

[55] Generalization an...

886播放

25:39

Generalization and Safety in Reinforcement Learning and Control: Aviv Tamar（上）

[56] Generalization an...

544播放

25:40

Generalization and Safety in Reinforcement Learning and Control: Aviv Tamar（中）

[57] Generalization an...

838播放

25:33

Generalization and Safety in Reinforcement Learning and Control: Aviv Tamar（下）

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