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Checked by:
Category:
Language:
English
Total Size:
733.1 MB
Info Hash:
5743EEB7D00724621857FF1C05B45E1C32453D4A
Added By:
Added:
Oct. 23, 2023, 3:03 p.m.
Stats:
|
(Last updated: May 16, 2025, 6:20 p.m.)
| File | Size |
|---|---|
| Get Bonus Downloads Here.url | 183 bytes |
| 001 Introduction.html | 70 bytes |
| 002 Reinforcement Learning series.html | 699 bytes |
| 003 Google Colab.mp4 | 5.8 MB |
| 003 Google Colab_en.vtt | 1.7 KB |
| 004 Where to begin.html | 70 bytes |
| 001 Elements common to all control tasks.mp4 | 38.7 MB |
| 001 Elements common to all control tasks_en.vtt | 6.0 KB |
| 002 The Markov decision process (MDP).mp4 | 25.1 MB |
| 002 The Markov decision process (MDP)_en.vtt | 5.6 KB |
| 003 Types of Markov decision process.mp4 | 8.7 MB |
| 003 Types of Markov decision process_en.vtt | 2.2 KB |
| 004 Trajectory vs episode.mp4 | 4.9 MB |
| 004 Trajectory vs episode_en.vtt | 1.1 KB |
| 005 Reward vs Return.mp4 | 5.3 MB |
| 005 Reward vs Return_en.vtt | 1.6 KB |
| 006 Discount factor.mp4 | 14.8 MB |
| 006 Discount factor_en.vtt | 4.1 KB |
| 007 Policy.mp4 | 7.4 MB |
| 007 Policy_en.vtt | 2.1 KB |
| 008 State values v(s) and action values q(s,a).mp4 | 4.3 MB |
| 008 State values v(s) and action values q(s,a)_en.vtt | 1.2 KB |
| 009 Bellman equations.mp4 | 12.4 MB |
| 009 Bellman equations_en.vtt | 3.0 KB |
| 010 Solving a Markov decision process.mp4 | 14.1 MB |
| 010 Solving a Markov decision process_en.vtt | 3.2 KB |
| 001 Monte Carlo methods.mp4 | 13.7 MB |
| 001 Monte Carlo methods_en.vtt | 3.3 KB |
| 002 Solving control tasks with Monte Carlo methods.mp4 | 23.8 MB |
| 002 Solving control tasks with Monte Carlo methods_en.vtt | 7.0 KB |
| 003 On-policy Monte Carlo control.mp4 | 20.4 MB |
| 003 On-policy Monte Carlo control_en.vtt | 4.6 KB |
| 001 Temporal difference methods.mp4 | 12.6 MB |
| 001 Temporal difference methods_en.vtt | 3.6 KB |
| 002 Solving control tasks with temporal difference methods.mp4 | 14.5 MB |
| 002 Solving control tasks with temporal difference methods_en.vtt | 3.6 KB |
| 003 Monte Carlo vs temporal difference methods.mp4 | 8.9 MB |
| 003 Monte Carlo vs temporal difference methods_en.vtt | 1.6 KB |
| 004 SARSA.mp4 | 17.8 MB |
| 004 SARSA_en.vtt | 3.9 KB |
| 005 Q-Learning.mp4 | 11.1 MB |
| 005 Q-Learning_en.vtt | 2.5 KB |
| 006 Advantages of temporal difference methods.mp4 | 3.7 MB |
| 006 Advantages of temporal difference methods_en.vtt | 1.2 KB |
| 001 N-step temporal difference methods.mp4 | 12.5 MB |
| 001 N-step temporal difference methods_en.vtt | 3.4 KB |
| 002 Where do n-step methods fit.mp4 | 11.1 MB |
| 002 Where do n-step methods fit_en.vtt | 2.7 KB |
| 003 Effect of changing n.mp4 | 28.0 MB |
| 003 Effect of changing n_en.vtt | 4.6 KB |
| 001 Function approximators.mp4 | 36.3 MB |
| 001 Function approximators_en.vtt | 8.6 KB |
| 002 Artificial Neural Networks.mp4 | 24.4 MB |
| 002 Artificial Neural Networks_en.vtt | 3.9 KB |
| 003 Artificial Neurons.mp4 | 25.6 MB |
| 003 Artificial Neurons_en.vtt | 5.8 KB |
| 004 How to represent a Neural Network.mp4 | 38.2 MB |
| 004 How to represent a Neural Network_en.vtt | 7.3 KB |
| 005 Stochastic Gradient Descent.mp4 | 49.8 MB |
| 005 Stochastic Gradient Descent_en.vtt | 6.4 KB |
| 006 Neural Network optimization.mp4 | 23.4 MB |
| 006 Neural Network optimization_en.vtt | 4.4 KB |
| 001 Policy gradient methods.mp4 | 21.7 MB |
| 001 Policy gradient methods_en.vtt | 4.7 KB |
| 002 Representing policies using neural networks.mp4 | 27.8 MB |
| 002 Representing policies using neural networks_en.vtt | 5.2 KB |
| 003 Policy performance.mp4 | 8.5 MB |
| 003 Policy performance_en.vtt | 2.6 KB |
| 004 The policy gradient theorem.mp4 | 15.9 MB |
| 004 The policy gradient theorem_en.vtt | 3.8 KB |
| 005 REINFORCE.mp4 | 13.2 MB |
| 005 REINFORCE_en.vtt | 4.1 KB |
| 006 Parallel learning.mp4 | 12.3 MB |
| 006 Parallel learning_en.vtt | 3.6 KB |
| 007 Entropy regularization.mp4 | 23.2 MB |
| 007 Entropy regularization_en.vtt | 6.6 KB |
| 008 REINFORCE 2.mp4 | 10.9 MB |
| 008 REINFORCE 2_en.vtt | 2.4 KB |
| 001 PyTorch Lightning.mp4 | 32.0 MB |
| 001 PyTorch Lightning_en.vtt | 9.3 KB |
| 002 Link to the code notebook.html | 70 bytes |
| 001 REINFORCE for continuous action spaces.html | 70 bytes |
| 001 A2C.mp4 | 50.1 MB |
| 001 A2C_en.vtt | 10.6 KB |
| 001 Generalized Advantage Estimation.html | 70 bytes |
| 001 Proximal Policy Optimization.html | 70 bytes |
| 001 Phasic PPO.html | 70 bytes |
| Bonus Resources.txt | 386 bytes |
Name
DL
Uploader
Size
S/L
Added
-
2.7 GB
[11
/
3]
2025-01-13
| Uploaded by FreeCourseWeb | Size 2.7 GB | Health [ 11 /3 ] | Added 2025-01-13 |
-
3.8 GB
[3
/
17]
2024-10-24
| Uploaded by FreeCourseWeb | Size 3.8 GB | Health [ 3 /17 ] | Added 2024-10-24 |
-
1.7 GB
[20
/
5]
2024-02-29
| Uploaded by FreeCourseWeb | Size 1.7 GB | Health [ 20 /5 ] | Added 2024-02-29 |
-
354.4 MB
[19
/
10]
2024-02-15
| Uploaded by FreeCourseWeb | Size 354.4 MB | Health [ 19 /10 ] | Added 2024-02-15 |
NOTE
SOURCE: Udemy Advanced Reinforcement Learning policy gradient methods
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COVER
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