Download Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC torrent

Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC

Torrent:	Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC
Description:	Advanced Reinforcement Learning in Python: from DQN to SAC https://DevCourseWeb.com MP4 \| Video: h264, 1280x720 \| Audio: AAC, 44.1 KHz Language: English \| Size: 2.41 GB \| Duration: 8h 5m Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: DDPG, TD3, SAC, NAF, HER. 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, Brax, 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 Advanced Reinforcement Learning course 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.
Downloads:	44
Category:	Other /Tutorials
Size:	2.4 GB
Show Files »	[ DevCourseWeb.com ] Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 01 - Introduction 001 Introduction.mp4 (24.3 MB) 001 Introduction_en.vtt (6.2 KB) 002 Reinforcement Learning series.html (0.5 KB) 003 Google Colab.mp4 (5.8 MB) 003 Google Colab_en.vtt (1.8 KB) 004 Where to begin.mp4 (5.1 MB) 004 Where to begin_en.vtt (2.0 KB) external-assets-links.txt (0.1 KB) 02 - Refresher The Markov Decision Process (MDP) 001 Module Overview.mp4 (2.6 MB) 001 Module Overview_en.vtt (1.0 KB) 002 Elements common to all control tasks.mp4 (38.7 MB) 002 Elements common to all control tasks_en.vtt (5.9 KB) 003 The Markov decision process (MDP).mp4 (25.1 MB) 003 The Markov decision process (MDP)_en.vtt (5.7 KB) 004 Types of Markov decision process.mp4 (8.7 MB) 004 Types of Markov decision process_en.vtt (2.2 KB) 005 Trajectory vs episode.mp4 (4.9 MB) 005 Trajectory vs episode_en.vtt (1.1 KB) 006 Reward vs Return.mp4 (5.3 MB) 006 Reward vs Return_en.vtt (1.6 KB) 007 Discount factor.mp4 (14.8 MB) 007 Discount factor_en.vtt (4.0 KB) 008 Policy.mp4 (7.4 MB) 008 Policy_en.vtt (2.2 KB) 009 State values v(s) and action values q(s,a).mp4 (4.3 MB) 009 State values v(s) and action values q(s,a)_en.vtt (1.2 KB) 010 Bellman equations.mp4 (12.4 MB) 010 Bellman equations_en.vtt (3.0 KB) 011 Solving a Markov decision process.mp4 (14.1 MB) 011 Solving a Markov decision process_en.vtt (3.1 KB) external-assets-links.txt (0.1 KB) 03 - Refresher Q-Learning 001 Module overview.mp4 (1.5 MB) 001 Module overview_en.vtt (0.7 KB) 002 Temporal difference methods.mp4 (12.6 MB) 002 Temporal difference methods_en.vtt (3.5 KB) 003 Solving control tasks with temporal difference methods.mp4 (14.5 MB) 003 Solving control tasks with temporal difference methods_en.vtt (3.6 KB) 004 Q-Learning.mp4 (11.1 MB) 004 Q-Learning_en.vtt (2.5 KB) 005 Advantages of temporal difference methods.mp4 (3.7 MB) 005 Advantages of temporal difference methods_en.vtt (1.2 KB) external-assets-links.txt (0.1 KB) 04 - Refresher Brief introduction to Neural Networks 001 Module overview.mp4 (1.8 MB) 001 Module overview_en.vtt (0.7 KB) 002 Function approximators.mp4 (36.3 MB) 002 Function approximators_en.vtt (8.5 KB) 003 Artificial Neural Networks.mp4 (24.3 MB) 003 Artificial Neural Networks_en.vtt (3.8 KB) 004 Artificial Neurons.mp4 (25.6 MB) 004 Artificial Neurons_en.vtt (5.8 KB) 005 How to represent a Neural Network.mp4 (38.2 MB) 005 How to represent a Neural Network_en.vtt (7.2 KB) 006 Stochastic Gradient Descent.mp4 (49.9 MB) 006 Stochastic Gradient Descent_en.vtt (6.3 KB) 007 Neural Network optimization.mp4 (23.4 MB) 007 Neural Network optimization_en.vtt (4.4 KB) external-assets-links.txt (0.1 KB) 05 - Refresher Deep Q-Learning 001 Module overview.mp4 (1.3 MB) 001 Module overview_en.vtt (0.5 KB) 002 Deep Q-Learning.mp4 (16.2 MB) 002 Deep Q-Learning_en.vtt (2.9 KB) 003 Experience Replay.mp4 (9.0 MB) 003 Experience Replay_en.vtt (2.2 KB) 004 Target Network.mp4 (16.6 MB) 004 Target Network_en.vtt (3.9 KB) external-assets-links.txt (0.1 KB) 06 - PyTorch Lightning 001 PyTorch Lightning.mp4 (32.0 MB) 001 PyTorch Lightning_en.vtt (9.2 KB) 002 Link to the code notebook.html (0.3 KB) 003 Introduction to PyTorch Lightning.mp4 (30.9 MB) 003 Introduction to PyTorch Lightning_en.vtt (6.2 KB) 004 Create the Deep Q-Network.mp4 (22.9 MB) 004 Create the Deep Q-Network_en.vtt (5.1 KB) 005 Create the policy.mp4 (18.0 MB) 005 Create the policy_en.vtt (5.1 KB) 006 Create the replay buffer.mp4 (23.0 MB) 006 Create the replay buffer_en.vtt (5.6 KB) 007 Create the environment.mp4 (32.2 MB) 007 Create the environment_en.vtt (7.5 KB) 008 Define the class for the Deep Q-Learning algorithm.mp4 (54.5 MB) 008 Define the class for the Deep Q-Learning algorithm_en.vtt (11.6 KB) 009 Define the play_episode() function.mp4 (29.1 MB) 009 Define the play_episode() function_en.vtt (4.9 KB) 010 Prepare the data loader and the optimizer.mp4 (30.4 MB) 010 Prepare the data loader and the optimizer_en.vtt (4.2 KB) 011 Define the train_step() method.mp4 (49.8 MB) 011 Define the train_step() method_en.vtt (9.3 KB) 012 Define the train_epoch_end() method.mp4 (32.2 MB) 012 Define the train_epoch_end() method_en.vtt (4.0 KB) 013 [Important] Lecture correction.html (0.6 KB) 014 Train the Deep Q-Learning algorithm.mp4 (35.0 MB) 014 Train the Deep Q-Learning algorithm_en.vtt (6.5 KB) 015 Explore the resulting agent.mp4 (20.3 MB) 015 Explore the resulting agent_en.vtt (2.8 KB) external-assets-links.txt (0.1 KB) 07 - Hyperparameter tuning with Optuna 001 Hyperparameter tuning with Optuna.mp4 (32.4 MB) 001 Hyperparameter tuning with Optuna_en.vtt (9.6 KB) 002 Link to the code notebook.html (0.3 KB) 003 Log average return.mp4 (33.6 MB) 003 Log average return_en.vtt (4.8 KB) 004 Define the objective function.mp4 (29.8 MB) 004 Define the objective function_en.vtt (5.3 KB) 005 Create and launch the hyperparameter tuning job.mp4 (18.5 MB files
Added:	2022-04-22 10:03:13
Language:	English
Peers:	Seeders : 9 , Leechers : 9
Tags:	Development Data Science Udemy
Release name:	Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC
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