DevCourseWeb com Udemy Advanced Reinforcement Learning in Python cutting edge DQNs

DevCourseWeb com Udemy Advanced Reinforcement Learning in Python cutting edge DQNs

Name	[ DevCourseWeb.com ] Udemy - Advanced Reinforcement Learning in Python - cutting-edge DQNs	DOWNLOAD Copy Link
Total Size	1.7 GB
Total Files	142
Last Seen	2024-11-02 23:56
Hash	AEEE5EFD07E93D9CDFD0364549553EB4EF7059D7

/
Get Bonus Downloads Here.url	0.2 KB
/1. Introduction/
1. Introduction.mp4	33.9 MB
1. Introduction.mp4.jpg	179.0 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	6.1 MB
3. Google Colab.srt	2.0 KB
4. Where to begin.mp4	4.8 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	72.5 MB
3. DQN for visual inputs.srt	15.5 KB
4. Prioritized Experience Repay Buffer.mp4	66.7 MB
4. Prioritized Experience Repay Buffer.srt	15.4 KB
5. Create the environment.mp4	65.6 MB
5. Create the environment.srt	14.3 KB
6. Implement the Deep Q-Learning algorithm with Prioritized Experience Replay.mp4	66.4 MB
6. Implement the Deep Q-Learning algorithm with Prioritized Experience Replay.srt	13.2 KB
7. Launch the training process.mp4	44.6 MB
7. Launch the training process.srt	5.9 KB
8. Check the resulting agent.mp4	17.6 MB
8. Check the resulting agent.srt	2.0 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	2.7 MB
1. Module overview.srt	1.2 KB
10. Bellman equations.mp4	13.0 MB
10. Bellman equations.srt	3.5 KB
11. Solving a Markov decision process.mp4	14.8 MB
11. Solving a Markov decision process.srt	3.7 KB
2. Elements common to all control tasks.mp4	40.6 MB
2. Elements common to all control tasks.srt	7.0 KB
3. The Markov decision process (MDP).mp4	26.3 MB
3. The Markov decision process (MDP).srt	6.5 KB
4. Types of Markov decision process.mp4	9.1 MB
4. Types of Markov decision process.srt	2.5 KB
5. Trajectory vs episode.mp4	5.2 MB
5. Trajectory vs episode.srt	1.3 KB
6. Reward vs Return.mp4	5.6 MB
6. Reward vs Return.srt	1.9 KB
7. Discount factor.mp4	15.5 MB
7. Discount factor.srt	4.7 KB
8. Policy.mp4	7.8 MB
8. Policy.srt	2.4 KB
9. State values v(s) and action values q(s,a).mp4	4.5 MB
9. State values v(s) and action values q(s,a).srt	1.3 KB
/3. Refresher Q-Learning/
1. Module overview.mp4	1.6 MB
1. Module overview.srt	0.8 KB
2. Temporal difference methods.mp4	13.2 MB
2. Temporal difference methods.srt	4.2 KB
3. Solving control tasks with temporal difference method.mp4	15.2 MB
3. Solving control tasks with temporal difference method.srt	4.2 KB
4. Q-Learning.mp4	11.6 MB
4. Q-Learning.srt	2.9 KB
5. Advantages of temporal difference methods.mp4	3.9 MB
5. Advantages of temporal difference methods.srt	1.3 KB
/.../4. Refresher Brief introduction to Neural Networks/
1. Module overview.mp4	1.9 MB
1. Module overview.srt	0.9 KB
2. Function approximators.mp4	38.1 MB
2. Function approximators.srt	10.0 KB
3. Artificial Neural Networks.mp4	25.5 MB
3. Artificial Neural Networks.srt	4.5 KB
4. Artificial Neurons.mp4	26.9 MB
4. Artificial Neurons.srt	6.7 KB
5. How to represent a Neural Network.mp4	40.0 MB
5. How to represent a Neural Network.srt	8.4 KB
6. Stochastic Gradient Descent.mp4	52.3 MB
6. Stochastic Gradient Descent.srt	7.4 KB
7. Neural Network optimization.mp4	24.5 MB
7. Neural Network optimization.srt	5.1 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	17.0 MB
2. Deep Q-Learning.srt	3.4 KB
3. Experience replay.mp4	9.4 MB
3. Experience replay.srt	2.6 KB
4. Target Network.mp4	17.4 MB
4. Target Network.srt	4.7 KB
/6. PyTorch Lightning/
1. PyTorch Lightning.mp4	33.6 MB
1. PyTorch Lightning.srt	10.7 KB
10. Prepare the data loader and the optimizer.mp4	31.9 MB
10. Prepare the data loader and the optimizer.srt	5.0 KB
11. Define the train_step() method.mp4	52.2 MB
11. Define the train_step() method.srt	11.1 KB
12. Define the train_epoch_end() method.mp4	33.7 MB
12. Define the train_epoch_end() method.srt	4.8 KB
13. Train the Deep Q-Learning algorithm.mp4	36.8 MB
13. Train the Deep Q-Learning algorithm.srt	7.7 KB
14. Explore the resulting agent.mp4	21.3 MB
14. Explore the resulting agent.srt	3.7 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	32.4 MB
3. Introduction to PyTorch Lightning.srt	7.1 KB
4. Create the Deep Q-Network.mp4	24.0 MB
4. Create the Deep Q-Network.srt	6.1 KB
5. Create the policy.mp4	18.9 MB
5. Create the policy.srt	5.9 KB
6. Create the replay buffer.mp4	24.1 MB
6. Create the replay buffer.srt	6.7 KB
7. Create the environment.mp4	33.8 MB
7. Create the environment.srt	9.1 KB
8. Define the class for the Deep Q-Learning algorithm.mp4	57.2 MB
8. Define the class for the Deep Q-Learning algorithm.srt	14.0 KB
9. Define the play_episode() function.mp4	30.5 MB
9. Define the play_episode() function.srt	5.6 KB
/.../7. Hyperparameter tuning with Optuna/
1. Hyperparameter tuning with Optuna.mp4	34.0 MB
1. Hyperparameter tuning with Optuna.srt	11.2 KB
2. Link to the code notebook.html	0.2 KB
2.1 Google colab.html	0.2 KB
3. Log average return.mp4	35.3 MB
3. Log average return.srt	5.7 KB
4. Define the objective function.mp4	31.3 MB
4. Define the objective function.srt	6.3 KB
5. Create and launch the hyperparameter tuning job.mp4	19.4 MB
5. Create and launch the hyperparameter tuning job.srt	3.3 KB
6. Explore the best trial.mp4	20.1 MB
6. Explore the best trial.srt	3.1 KB
/.../8. Double Deep Q-Learning/
1. Maximization bias and Double Deep Q-Learning.mp4	14.5 MB
2. Link to the code notebook.html	0.2 KB
2.1 Google colab.html	0.2 KB
3. Create the Double Deep Q-Learning algorithm.mp4	52.4 MB
3. Create the Double Deep Q-Learning algorithm.srt	8.7 KB
4. Check the resulting agent.mp4	9.6 MB
4. Check the resulting agent.srt	1.8 KB
/.../9. Dueling Deep Q-Networks/
1. Dueling Deep Q-Networks.html	0.1 KB
2. Link to the code notebook.html	0.2 KB
2.1 Google colab.html	0.2 KB
3. Create the dueling DQN.mp4	57.0 MB
3. Create the dueling DQN.srt	11.9 KB
4. Create the environment - Part 1.mp4	43.3 MB
4. Create the environment - Part 1.srt	9.2 KB
5. Create the environment - Part 2.mp4	38.4 MB
5. Create the environment - Part 2.srt	6.8 KB
6. Implement Deep Q-Learning.mp4	38.2 MB
6. Implement Deep Q-Learning.srt	6.8 KB
7. Check the resulting agent.mp4	22.0 MB
7. Check the resulting agent.srt	2.8 KB
/~Get Your Files Here !/
Bonus Resources.txt	0.4 KB
Total files 142