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[ DevCourseWeb com ] Udemy - Advanced Reinforcement Learning in Python - cutting-edge DQNs
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1. Introduction
1. Introduction.mp4
1. Introduction.mp4.jpg
1.1 Advanced Reinforcement Learning in Python from DQN to SAC.html
1.2 Reinforcement Learning beginner to master.html
2. Reinforcement Learning series.html
3. Google Colab.mp4
3. Google Colab.srt
4. Where to begin.mp4
4. Where to begin.srt
10. Prioritized Experience Replay
1. Prioritized Experience Replay.html
2. Link to the code notebook.html
3. DQN for visual inputs.mp4
3. DQN for visual inputs.srt
4. Prioritized Experience Repay Buffer.mp4
4. Prioritized Experience Repay Buffer.srt
5. Create the environment.mp4
5. Create the environment.srt
6. Implement the Deep Q-Learning algorithm with Prioritized Experience Replay.mp4
6. Implement the Deep Q-Learning algorithm with Prioritized Experience Replay.srt
7. Launch the training process.mp4
7. Launch the training process.srt
8. Check the resulting agent.mp4
8. Check the resulting agent.srt
11. Noisy Deep Q-Networks
1. Noisy Deep Q-Networks.html
12. N-step Deep Q-Learning
1. N-step Deep Q-Learning.html
13. Distributional Deep Q-Networks
1. Distributional Deep Q-Networks.html
2. Refresher The Markov Decision Process (MDP)
1. Module overview.mp4
1. Module overview.srt
10. Bellman equations.mp4
10. Bellman equations.srt
11. Solving a Markov decision process.mp4
11. Solving a Markov decision process.srt
2. Elements common to all control tasks.mp4
2. Elements common to all control tasks.srt
3. The Markov decision process (MDP).mp4
3. The Markov decision process (MDP).srt
4. Types of Markov decision process.mp4
4. Types of Markov decision process.srt
5. Trajectory vs episode.mp4
5. Trajectory vs episode.srt
6. Reward vs Return.mp4
6. Reward vs Return.srt
7. Discount factor.mp4
7. Discount factor.srt
8. Policy.mp4
8. Policy.srt
9. State values v(s) and action values q(s,a).mp4
9. State values v(s) and action values q(s,a).srt
3. Refresher Q-Learning
1. Module overview.mp4
1. Module overview.srt
2. Temporal difference methods.mp4
2. Temporal difference methods.srt
3. Solving control tasks with temporal difference method.mp4
3. Solving control tasks with temporal difference method.srt
4. Q-Learning.mp4
4. Q-Learning.srt
5. Advantages of temporal difference methods.mp4
5. Advantages of temporal difference methods.srt
4. Refresher Brief introduction to Neural Networks
1. Module overview.mp4
1. Module overview.srt
2. Function approximators.mp4
2. Function approximators.srt
3. Artificial Neural Networks.mp4
3. Artificial Neural Networks.srt
4. Artificial Neurons.mp4
4. Artificial Neurons.srt
5. How to represent a Neural Network.mp4
5. How to represent a Neural Network.srt
6. Stochastic Gradient Descent.mp4
6. Stochastic Gradient Descent.srt
7. Neural Network optimization.mp4
7. Neural Network optimization.srt
5. Refresher Deep Q-Learning
1. Module overview.mp4
1. Module overview.srt
2. Deep Q-Learning.mp4
2. Deep Q-Learning.srt
3. Experience replay.mp4
3. Experience replay.srt
4. Target Network.mp4
4. Target Network.srt
6. PyTorch Lightning
1. PyTorch Lightning.mp4
1. PyTorch Lightning.srt
10. Prepare the data loader and the optimizer.mp4
10. Prepare the data loader and the optimizer.srt
11. Define the train_step() method.mp4
11. Define the train_step() method.srt
12. Define the train_epoch_end() method.mp4
12. Define the train_epoch_end() method.srt
13. Train the Deep Q-Learning algorithm.mp4
13. Train the Deep Q-Learning algorithm.srt
14. Explore the resulting agent.mp4
14. Explore the resulting agent.srt
2. Link to the code notebook.html
2.1 Google colab.html
3. Introduction to PyTorch Lightning.mp4
3. Introduction to PyTorch Lightning.srt
4. Create the Deep Q-Network.mp4
4. Create the Deep Q-Network.srt
5. Create the policy.mp4
5. Create the policy.srt
6. Create the replay buffer.mp4
6. Create the replay buffer.srt
7. Create the environment.mp4
7. Create the environment.srt
8. Define the class for the Deep Q-Learning algorithm.mp4
8. Define the class for the Deep Q-Learning algorithm.srt
9. Define the play_episode() function.mp4
9. Define the play_episode() function.srt
7. Hyperparameter tuning with Optuna
1. Hyperparameter tuning with Optuna.mp4
1. Hyperparameter tuning with Optuna.srt
2. Link to the code notebook.html
2.1 Google colab.html
3. Log average return.mp4
3. Log average return.srt
4. Define the objective function.mp4
4. Define the objective function.srt
5. Create and launch the hyperparameter tuning job.mp4
5. Create and launch the hyperparameter tuning job.srt
6. Explore the best trial.mp4
6. Explore the best trial.srt
8. Double Deep Q-Learning
1. Maximization bias and Double Deep Q-Learning.mp4
2. Link to the code notebook.html
2.1 Google colab.html
3. Create the Double Deep Q-Learning algorithm.mp4
3. Create the Double Deep Q-Learning algorithm.srt
4. Check the resulting agent.mp4
4. Check the resulting agent.srt
9. Dueling Deep Q-Networks
1. Dueling Deep Q-Networks.html
2. Link to the code notebook.html
2.1 Google colab.html
3. Create the dueling DQN.mp4
3. Create the dueling DQN.srt
4. Create the environment - Part 1.mp4
4. Create the environment - Part 1.srt
5. Create the environment - Part 2.mp4
5. Create the environment - Part 2.srt
6. Implement Deep Q-Learning.mp4
6. Implement Deep Q-Learning.srt
7. Check the resulting agent.mp4
7. Check the resulting agent.srt
Bonus Resources.txt
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