| 001 A2C.mp4 | 50.1 MB | ||
| 001 A2C_en.vtt | 10.6 KB | ||
| 001 Elements common to all control tasks.mp4 | 38.7 MB | ||
| 001 Elements common to all control tasks_en.vtt | 6 KB | ||
| 001 Function approximators.mp4 | 36.3 MB | ||
| 001 Function approximators_en.vtt | 8.6 KB | ||
| 001 Generalized Advantage Estimation.html | 102.4 B | ||
| 001 Introduction.html | 102.4 B | ||
| 001 Monte Carlo methods.mp4 | 13.7 MB | ||
| 001 Monte Carlo methods_en.vtt | 3.3 KB | ||
| 001 N-step temporal difference methods.mp4 | 12.5 MB | ||
| 001 N-step temporal difference methods_en.vtt | 3.4 KB | ||
| 001 Phasic PPO.html | 102.4 B | ||
| 001 Policy gradient methods.mp4 | 21.7 MB | ||
| 001 Policy gradient methods_en.vtt | 4.7 KB | ||
| 001 Proximal Policy Optimization.html | 102.4 B | ||
| 001 PyTorch Lightning.mp4 | 32 MB | ||
| 001 PyTorch Lightning_en.vtt | 9.3 KB | ||
| 001 REINFORCE for continuous action spaces.html | 102.4 B | ||
| 001 Temporal difference methods.mp4 | 12.6 MB | ||
| 001 Temporal difference methods_en.vtt | 3.6 KB | ||
| 002 Artificial Neural Networks.mp4 | 24.4 MB | ||
| 002 Artificial Neural Networks_en.vtt | 3.9 KB | ||
| 002 Link to the code notebook.html | 102.4 B | ||
| 002 Reinforcement Learning series.html | 716.8 B | ||
| 002 Representing policies using neural networks.mp4 | 27.8 MB | ||
| 002 Representing policies using neural networks_en.vtt | 5.2 KB | ||
| 002 Solving control tasks with Monte Carlo methods.mp4 | 23.8 MB | ||
| 002 Solving control tasks with Monte Carlo methods_en.vtt | 7 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 | ||
| 002 The Markov decision process (MDP).mp4 | 25.1 MB | ||
| 002 The Markov decision process (MDP)_en.vtt | 5.6 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 Artificial Neurons.mp4 | 25.6 MB | ||
| 003 Artificial Neurons_en.vtt | 5.8 KB | ||
| 003 Effect of changing n.mp4 | 28 MB | ||
| 003 Effect of changing n_en.vtt | 4.6 KB | ||
| 003 Google Colab.mp4 | 5.8 MB | ||
| 003 Google Colab_en.vtt | 1.7 KB | ||
| 003 Monte Carlo vs temporal difference methods.mp4 | 8.9 MB | ||
| 003 Monte Carlo vs temporal difference methods_en.vtt | 1.6 KB | ||
| 003 On-policy Monte Carlo control.mp4 | 20.4 MB | ||
| 003 On-policy Monte Carlo control_en.vtt | 4.6 KB | ||
| 003 Policy performance.mp4 | 8.5 MB | ||
| 003 Policy performance_en.vtt | 2.6 KB | ||
| 003 Types of Markov decision process.mp4 | 8.7 MB | ||
| 003 Types of Markov decision process_en.vtt | 2.2 KB | ||
| 004 How to represent a Neural Network.mp4 | 38.2 MB | ||
| 004 How to represent a Neural Network_en.vtt | 7.3 KB | ||
| 004 SARSA.mp4 | 17.8 MB | ||
| 004 SARSA_en.vtt | 3.9 KB | ||
| 004 The policy gradient theorem.mp4 | 15.9 MB | ||
| 004 The policy gradient theorem_en.vtt | 3.8 KB | ||
| 004 Trajectory vs episode.mp4 | 4.9 MB | ||
| 004 Trajectory vs episode_en.vtt | 1.1 KB | ||
| 004 Where to begin.html | 102.4 B | ||
| 005 Q-Learning.mp4 | 11.1 MB | ||
| 005 Q-Learning_en.vtt | 2.5 KB | ||
| 005 REINFORCE.mp4 | 13.2 MB | ||
| 005 REINFORCE_en.vtt | 4.1 KB | ||
| 005 Reward vs Return.mp4 | 5.3 MB | ||
| 005 Reward vs Return_en.vtt | 1.6 KB | ||
| 005 Stochastic Gradient Descent.mp4 | 49.8 MB | ||
| 005 Stochastic Gradient Descent_en.vtt | 6.4 KB | ||
| 006 Advantages of temporal difference methods.mp4 | 3.7 MB | ||
| 006 Advantages of temporal difference methods_en.vtt | 1.2 KB | ||
| 006 Discount factor.mp4 | 14.8 MB | ||
| 006 Discount factor_en.vtt | 4.1 KB | ||
| 006 Neural Network optimization.mp4 | 23.4 MB | ||
| 006 Neural Network optimization_en.vtt | 4.4 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 | ||
| 007 Policy.mp4 | 7.4 MB | ||
| 007 Policy_en.vtt | 2.1 KB | ||
| 008 REINFORCE 2.mp4 | 10.9 MB | ||
| 008 REINFORCE 2_en.vtt | 2.4 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 KB | ||
| 010 Solving a Markov decision process.mp4 | 14.1 MB | ||
| 010 Solving a Markov decision process_en.vtt | 3.2 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 88 total files | |||
Advanced Reinforcement Learning: policy gradient methods
https://DevCourseWeb.com
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 48.0 KHz
Language: English | Size: 733 MB | Duration: 47 lectures • 2h 35m
Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: (REINFORCE, A2C, PPO, etc)
What you'll learn
Master some of the most advanced Reinforcement Learning algorithms.
Learn how to create AIs that can act in a complex environment to achieve their goals.
Create from scratch advanced Reinforcement Learning agents using Python's most popular tools (PyTorch Lightning, OpenAI gym, Optuna)
Learn how to perform hyperparameter tuning (Choosing the best experimental conditions for our AI to learn)
Fundamentally understand the learning process for each algorithm.
Debug and extend the algorithms presented.
Understand and implement new algorithms from research papers.
Requirements
Be comfortable programming in Python
Completing our course "Reinforcement Learning beginner to master" or being familiar with the basics of Reinforcement Learning (or watching the leveling sections included in this course).
Know basic statistics (mean, variance, normal distribution)
Description
This is the most complete Reinforcement Learning course series on Udemy. In it, you will learn to implement some of the most powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will learn to combine these techniques with Neural Networks and Deep Learning methods to create adaptive Artificial Intelligence agents capable of solving decision-making tasks.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 776.8 MB | freecoursewb | 1 week | 1 | 11 | |
| 2.2 GB | freecoursewb | 1 week | 91 | 12 | |
| 770.5 MB | freecoursewb | 1 week | 24 | 18 | |
| 699.6 MB | freecoursewb | 1 week | 28 | 5 | |
| 1 GB | freecoursewb | 2 weeks | 0 | 0 |
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