Udemy - Advanced Reinforcement Learning in Python - cutting-edge DQNs

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[ DevCourseWeb.com ] Udemy - Advanced Reinforcement Learning in Python - cutting-edge DQNs
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction
    • 1. Introduction.mp4 (32.4 MB)
    • 1. Introduction.mp4.jpg (174.8 KB)
    • 1.1 Advanced Reinforcement Learning in Python from DQN to SAC.html (0.1 KB)
    • 1.2 Reinforcement Learning beginner to master.html (0.1 KB)
    • 2. Reinforcement Learning series.html (0.4 KB)
    • 3. Google Colab.mp4 (5.8 MB)
    • 3. Google Colab.srt (2.0 KB)
    • 4. Where to begin.mp4 (4.6 MB)
    • 4. Where to begin.srt (2.1 KB)
    10. Prioritized Experience Replay
    • 1. Prioritized Experience Replay.html (0.1 KB)
    • 2. Link to the code notebook.html (0.1 KB)
    • 3. DQN for visual inputs.mp4 (69.1 MB)
    • 3. DQN for visual inputs.srt (15.1 KB)
    • 4. Prioritized Experience Repay Buffer.mp4 (63.6 MB)
    • 4. Prioritized Experience Repay Buffer.srt (15.0 KB)
    • 5. Create the environment.mp4 (62.6 MB)
    • 5. Create the environment.srt (14.0 KB)
    • 6. Implement the Deep Q-Learning algorithm with Prioritized Experience Replay.mp4 (63.3 MB)
    • 6. Implement the Deep Q-Learning algorithm with Prioritized Experience Replay.srt (12.9 KB)
    • 7. Launch the training process.mp4 (42.5 MB)
    • 7. Launch the training process.srt (5.8 KB)
    • 8. Check the resulting agent.mp4 (16.8 MB)
    • 8. Check the resulting agent.srt (1.9 KB)
    11. Noisy Deep Q-Networks
    • 1. Noisy Deep Q-Networks.html (0.1 KB)
    12. N-step Deep Q-Learning
    • 1. N-step Deep Q-Learning.html (0.1 KB)
    13. Distributional Deep Q-Networks
    • 1. Distributional Deep Q-Networks.html (0.1 KB)
    2. Refresher The Markov Decision Process (MDP)
    • 1. Module overview.mp4 (2.6 MB)
    • 1. Module overview.srt (1.1 KB)
    • 10. Bellman equations.mp4 (12.4 MB)
    • 10. Bellman equations.srt (3.4 KB)
    • 11. Solving a Markov decision process.mp4 (14.2 MB)
    • 11. Solving a Markov decision process.srt (3.6 KB)
    • 2. Elements common to all control tasks.mp4 (38.7 MB)
    • 2. Elements common to all control tasks.srt (6.8 KB)
    • 3. The Markov decision process (MDP).mp4 (25.1 MB)
    • 3. The Markov decision process (MDP).srt (6.4 KB)
    • 4. Types of Markov decision process.mp4 (8.7 MB)
    • 4. Types of Markov decision process.srt (2.4 KB)
    • 5. Trajectory vs episode.mp4 (4.9 MB)
    • 5. Trajectory vs episode.srt (1.2 KB)
    • 6. Reward vs Return.mp4 (5.3 MB)
    • 6. Reward vs Return.srt (1.8 KB)
    • 7. Discount factor.mp4 (14.8 MB)
    • 7. Discount factor.srt (4.6 KB)
    • 8. Policy.mp4 (7.4 MB)
    • 8. Policy.srt (2.3 KB)
    • 9. State values v(s) and action values q(s,a).mp4 (4.3 MB)
    • 9. State values v(s) and action values q(s,a).srt (1.3 KB)
    3. Refresher Q-Learning
    • 1. Module overview.mp4 (1.5 MB)
    • 1. Module overview.srt (0.8 KB)
    • 2. Temporal difference methods.mp4 (12.6 MB)
    • 2. Temporal difference methods.srt (4.1 KB)
    • 3. Solving control tasks with temporal difference method.mp4 (14.5 MB)
    • 3. Solving control tasks with temporal difference method.srt (4.1 KB)
    • 4. Q-Learning.mp4 (11.1 MB)
    • 4. Q-Learning.srt (2.9 KB)
    • 5. Advantages of temporal difference methods.mp4 (3.7 MB)
    • 5. Advantages of temporal difference methods.srt (1.3 KB)
    4. Refresher Brief introduction to Neural Networks
    • 1. Module overview.mp4 (1.8 MB)
    • 1. Module overview.srt (0.8 KB)
    • 2. Function approximators.mp4 (36.3 MB)
    • 2. Function approximators.srt (9.8 KB)
    • 3. Artificial Neural Networks.mp4 (24.3 MB)
    • 3. Artificial Neural Networks.srt (4.4 KB)
    • 4. Artificial Neurons.mp4 (25.6 MB)
    • 4. Artificial Neurons.srt (6.6 KB)
    • 5. How to represent a Neural Network.mp4 (38.2 MB)
    • 5. How to represent a Neural Network.srt (8.2 KB)
    • 6. Stochastic Gradient Descent.mp4 (49.9 MB)
    • 6. Stochastic Gradient Descent.srt (7.2 KB)
    • 7. Neural Network optimization.mp4 (23.4 MB)
    • 7. Neural Network optimization.srt (5.0 KB)
    5. Refresher Deep Q-Learning
    • 1. Module overview.mp4 (1.3 MB)
    • 1. Module overview.srt (0.6 KB)
    • 2. Deep Q-Learning.mp4 (16.2 MB)
    • 2. Deep Q-Learning.srt (3.4 KB)
    • 3. Experience replay.mp4 (9.0 MB)
    • 3. Experience replay.srt (2.5 KB)
    • 4. Target Network.mp4 (16.6 MB)
    • 4. Target Network.srt (4.6 KB)
    6. PyTorch Lightning
    • 1. PyTorch Lightning.mp4 (32.0 MB)
    • 1. PyTorch Lightning.srt (10.5 KB)
    • 10. Prepare the data loader and the optimizer.mp4 (30.4 MB)
    • 10. Prepare the data loader and the optimizer.srt (4.9 KB)
    • 11. Define the train_step() method.mp4 (49.8 MB)
    • 11. Define the train_step() method.srt (10.9 KB)
    • 12. Define the train_epoch_end() method.mp4 (32.2 MB)
    • 12. Define the train_epoch_end() method.srt (4.7 KB)
    • 13. Train the Deep Q-Learning algorithm.mp4 (35.1 MB)
    • 13. Train the Deep Q-Learning algorithm.srt (7.5 KB)
    • 14. Explore the resulting agent.mp4 (20.3 MB)
    • 14. Explore the resulting agent.srt (3.6 KB)
    • 2. Link to the code notebook.html (0.2 KB)
    • 2.1 Google colab.html (0.2 KB)
    • 3. Introduction to PyTorch Lightning.mp4 (30.9 MB)
    • 3. Introduction to PyTorch Lightning.srt (7.0 KB)
    • 4. Create the Deep Q-Network.mp4 (22.9 MB)
    • 4. Create the Deep Q-Network.srt (5.9 KB)
    • 5. Create the policy.mp4 (18.0 MB)
    • 5. Create the policy.srt (5.7 KB)
    • 6. Create the replay buffer.mp4 (23.0 MB)
    • 6. Create the replay buffer.srt (6.6 KB)
    • 7. Create the environment.mp4 (32.2 MB)
    • <

Description

Advanced Reinforcement Learning in Python: cutting-edge DQNs



https://DevCourseWeb.com

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 73 lectures (5h 9m) | Size: 1.6 GB

Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: From basic DQN to Rainbow DQN

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)



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Udemy - Advanced Reinforcement Learning in Python - cutting-edge DQNs
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