DevCourseWeb com Udemy Advanced Reinforcement Learning in Python from DQN to SAC

DevCourseWeb com Udemy Advanced Reinforcement Learning in Python from DQN to SAC

Name	[ DevCourseWeb.com ] Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	2.6 GB
Total Files	228
Last Seen	2025-09-09 23:22
Hash	E1676BD24ED4F26DA6DFDB9D5274227B5427AF5C

/
Get Bonus Downloads Here.url	0.2 KB
/01 - Introduction/
001 Introduction.mp4	25.5 MB
001 Introduction_en.vtt	6.3 KB
002 Reinforcement Learning series.html	0.5 KB
003 Google Colab.mp4	6.1 MB
003 Google Colab_en.vtt	1.8 KB
004 Where to begin.mp4	5.3 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	2.7 MB
001 Module Overview_en.vtt	1.0 KB
002 Elements common to all control tasks.mp4	40.6 MB
002 Elements common to all control tasks_en.vtt	6.1 KB
003 The Markov decision process (MDP).mp4	26.3 MB
003 The Markov decision process (MDP)_en.vtt	5.8 KB
004 Types of Markov decision process.mp4	9.1 MB
004 Types of Markov decision process_en.vtt	2.3 KB
005 Trajectory vs episode.mp4	5.2 MB
005 Trajectory vs episode_en.vtt	1.1 KB
006 Reward vs Return.mp4	5.5 MB
006 Reward vs Return_en.vtt	1.7 KB
007 Discount factor.mp4	15.5 MB
007 Discount factor_en.vtt	4.1 KB
008 Policy.mp4	7.8 MB
008 Policy_en.vtt	2.2 KB
009 State values v(s) and action values q(s,a).mp4	4.5 MB
009 State values v(s) and action values q(s,a)_en.vtt	1.2 KB
010 Bellman equations.mp4	13.0 MB
010 Bellman equations_en.vtt	3.1 KB
011 Solving a Markov decision process.mp4	14.8 MB
011 Solving a Markov decision process_en.vtt	3.2 KB
external-assets-links.txt	0.1 KB
/.../03 - Refresher Q-Learning/
001 Module overview.mp4	1.6 MB
001 Module overview_en.vtt	0.7 KB
002 Temporal difference methods.mp4	13.2 MB
002 Temporal difference methods_en.vtt	3.6 KB
003 Solving control tasks with temporal difference methods.mp4	15.2 MB
003 Solving control tasks with temporal difference methods_en.vtt	3.7 KB
004 Q-Learning.mp4	11.6 MB
004 Q-Learning_en.vtt	2.5 KB
005 Advantages of temporal difference methods.mp4	3.9 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	1.9 MB
001 Module overview_en.vtt	0.7 KB
002 Function approximators.mp4	38.1 MB
002 Function approximators_en.vtt	8.7 KB
003 Artificial Neural Networks.mp4	25.5 MB
003 Artificial Neural Networks_en.vtt	3.9 KB
004 Artificial Neurons.mp4	26.9 MB
004 Artificial Neurons_en.vtt	6.0 KB
005 How to represent a Neural Network.mp4	40.0 MB
005 How to represent a Neural Network_en.vtt	7.4 KB
006 Stochastic Gradient Descent.mp4	52.3 MB
006 Stochastic Gradient Descent_en.vtt	6.5 KB
007 Neural Network optimization.mp4	24.5 MB
007 Neural Network optimization_en.vtt	4.5 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	0.6 KB
002 Deep Q-Learning.mp4	17.0 MB
002 Deep Q-Learning_en.vtt	3.0 KB
003 Experience Replay.mp4	9.4 MB
003 Experience Replay_en.vtt	2.3 KB
004 Target Network.mp4	17.4 MB
004 Target Network_en.vtt	4.0 KB
external-assets-links.txt	0.1 KB
/.../06 - PyTorch Lightning/
001 PyTorch Lightning.mp4	33.6 MB
001 PyTorch Lightning_en.vtt	9.4 KB
002 Link to the code notebook.html	0.3 KB
003 Introduction to PyTorch Lightning.mp4	32.4 MB
003 Introduction to PyTorch Lightning_en.vtt	6.4 KB
004 Create the Deep Q-Network.mp4	24.0 MB
004 Create the Deep Q-Network_en.vtt	5.3 KB
005 Create the policy.mp4	18.9 MB
005 Create the policy_en.vtt	5.2 KB
006 Create the replay buffer.mp4	24.1 MB
006 Create the replay buffer_en.vtt	5.8 KB
007 Create the environment.mp4	33.8 MB
007 Create the environment_en.vtt	7.7 KB
008 Define the class for the Deep Q-Learning algorithm.mp4	57.2 MB
008 Define the class for the Deep Q-Learning algorithm_en.vtt	11.9 KB
009 Define the play_episode() function.mp4	30.5 MB
009 Define the play_episode() function_en.vtt	5.0 KB
010 Prepare the data loader and the optimizer.mp4	31.9 MB
010 Prepare the data loader and the optimizer_en.vtt	4.3 KB
011 Define the train_step() method.mp4	52.2 MB
011 Define the train_step() method_en.vtt	9.5 KB
012 Define the train_epoch_end() method.mp4	33.7 MB
012 Define the train_epoch_end() method_en.vtt	4.1 KB
013 [Important] Lecture correction.html	0.6 KB
014 Train the Deep Q-Learning algorithm.mp4	36.7 MB
014 Train the Deep Q-Learning algorithm_en.vtt	6.6 KB
015 Explore the resulting agent.mp4	21.2 MB
015 Explore the resulting agent_en.vtt	2.9 KB
external-assets-links.txt	0.1 KB
/.../07 - Hyperparameter tuning with Optuna/
001 Hyperparameter tuning with Optuna.mp4	34.0 MB
001 Hyperparameter tuning with Optuna_en.vtt	9.9 KB
002 Link to the code notebook.html	0.3 KB
003 Log average return.mp4	35.3 MB
003 Log average return_en.vtt	4.9 KB
004 Define the objective function.mp4	31.3 MB
004 Define the objective function_en.vtt	5.4 KB
005 Create and launch the hyperparameter tuning job.mp4	19.4 MB
005 Create and launch the hyperparameter tuning job_en.vtt	2.7 KB
006 Explore the best trial.mp4	20.1 MB
006 Explore the best trial_en.vtt	2.7 KB
external-assets-links.txt	0.1 KB
/.../08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/
001 Continuous action spaces.mp4	31.1 MB
001 Continuous action spaces_en.vtt	6.9 KB
002 The advantage function.mp4	14.1 MB
002 The advantage function_en.vtt	4.9 KB
003 Normalized Advantage Function (NAF).mp4	10.6 MB
003 Normalized Advantage Function (NAF)_en.vtt	3.4 KB
004 Normalized Advantage Function pseudocode.mp4	24.3 MB
004 Normalized Advantage Function pseudocode_en.vtt	5.9 KB
005 Link to the code notebook.html	0.3 KB
006 Hyperbolic tangent.mp4	4.9 MB
006 Hyperbolic tangent_en.vtt	1.6 KB
007 Creating the (NAF) Deep Q-Network 1.mp4	43.4 MB
007 Creating the (NAF) Deep Q-Network 1_en.vtt	7.6 KB
008 Creating the (NAF) Deep Q-Network 2.mp4	15.7 MB
008 Creating the (NAF) Deep Q-Network 2_en.vtt	3.3 KB
009 Creating the (NAF) Deep Q-Network 3.mp4	5.6 MB
009 Creating the (NAF) Deep Q-Network 3_en.vtt	1.1 KB
010 Creating the (NAF) Deep Q-Network 4.mp4	50.2 MB
010 Creating the (NAF) Deep Q-Network 4_en.vtt	9.5 KB
011 Creating the policy.mp4	26.3 MB
011 Creating the policy_en.vtt	5.3 KB
012 Create the environment.mp4	23.6 MB
012 Create the environment_en.vtt	4.7 KB
013 Polyak averaging.mp4	5.1 MB
013 Polyak averaging_en.vtt	1.5 KB
014 Implementing Polyak averaging.mp4	10.9 MB
014 Implementing Polyak averaging_en.vtt	2.3 KB
015 Create the (NAF) Deep Q-Learning algorithm.mp4	45.0 MB
015 Create the (NAF) Deep Q-Learning algorithm_en.vtt	8.1 KB
016 Implement the training step.mp4	13.9 MB
016 Implement the training step_en.vtt	2.5 KB
017 Implement the end-of-epoch logic.mp4	13.1 MB
017 Implement the end-of-epoch logic_en.vtt	2.3 KB
018 Debugging and launching the algorithm.mp4	21.0 MB
018 Debugging and launching the algorithm_en.vtt	2.9 KB
019 Checking the resulting agent.mp4	17.2 MB
019 Checking the resulting agent_en.vtt	2.0 KB
external-assets-links.txt	0.1 KB
/.../09 - Refresher Policy gradient methods/
001 Policy gradient methods.mp4	22.7 MB
001 Policy gradient methods_en.vtt	4.9 KB
002 Policy performance.mp4	8.9 MB
002 Policy performance_en.vtt	2.6 KB
003 Representing policies using neural networks.mp4	29.1 MB
003 Representing policies using neural networks_en.vtt	5.4 KB
004 The policy gradient theorem.mp4	16.7 MB
004 The policy gradient theorem_en.vtt	3.9 KB
005 Entropy Regularization.mp4	24.3 MB
005 Entropy Regularization_en.vtt	6.7 KB
/.../10 - Deep Deterministic Policy Gradient (DDPG)/
001 The Brax Physics engine.mp4	21.0 MB
001 The Brax Physics engine_en.vtt	3.6 KB
002 Deep Deterministic Policy Gradient (DDPG).mp4	33.9 MB
002 Deep Deterministic Policy Gradient (DDPG)_en.vtt	10.2 KB
003 DDPG pseudocode.mp4	21.9 MB
003 DDPG pseudocode_en.vtt	4.0 KB
004 Link to the code notebook.html	0.3 KB
005 Deep Deterministic Policy Gradient (DDPG).mp4	33.4 MB
005 Deep Deterministic Policy Gradient (DDPG)_en.vtt	5.8 KB
006 Create the gradient policy.mp4	45.6 MB
006 Create the gradient policy_en.vtt	10.0 KB
007 Create the Deep Q-Network.mp4	23.9 MB
007 Create the Deep Q-Network_en.vtt	4.4 KB
008 Create the DDPG class.mp4	40.7 MB
008 Create the DDPG class_en.vtt	7.5 KB
009 Define the play method.mp4	13.9 MB
009 Define the play method_en.vtt	2.2 KB
010 Setup the optimizers and dataloader.mp4	23.3 MB
010 Setup the optimizers and dataloader_en.vtt	3.3 KB
011 Define the training step.mp4	60.7 MB
011 Define the training step_en.vtt	10.1 KB
012 Launch the training process.mp4	35.9 MB
012 Launch the training process_en.vtt	4.0 KB
013 Check the resulting agent.mp4	31.7 MB
013 Check the resulting agent_en.vtt	1.7 KB
external-assets-links.txt	0.2 KB
/.../11 - Twin Delayed DDPG (TD3)/
001 Twin Delayed DDPG (TD3).mp4	35.6 MB
001 Twin Delayed DDPG (TD3)_en.vtt	11.7 KB
002 TD3 pseudocode.mp4	21.0 MB
002 TD3 pseudocode_en.vtt	4.4 KB
003 Link to code notebook.html	0.3 KB
004 Twin Delayed DDPG (TD3).mp4	20.9 MB
004 Twin Delayed DDPG (TD3)_en.vtt	3.3 KB
005 Clipped double Q-Learning.mp4	33.1 MB
005 Clipped double Q-Learning_en.vtt	4.0 KB
006 Delayed policy updates.mp4	12.7 MB
006 Delayed policy updates_en.vtt	2.1 KB
007 Target policy smoothing.mp4	32.5 MB
007 Target policy smoothing_en.vtt	4.2 KB
008 Check the resulting agent.mp4	32.6 MB
008 Check the resulting agent_en.vtt	2.3 KB
external-assets-links.txt	0.1 KB
/.../12 - Soft Actor-Critic (SAC)/
001 Soft Actor-Critic (SAC).mp4	25.1 MB
001 Soft Actor-Critic (SAC)_en.vtt	7.7 KB
002 SAC pseudocode.mp4	10.0 MB
002 SAC pseudocode_en.vtt	2.1 KB
003 Create the robotics task.mp4	77.6 MB
003 Create the robotics task_en.vtt	11.7 KB
004 Create the Deep Q-Network.mp4	19.9 MB
004 Create the Deep Q-Network_en.vtt	3.6 KB
005 Create the gradient policy.mp4	56.4 MB
005 Create the gradient policy_en.vtt	12.9 KB
006 Implement the Soft Actor-Critic algorithm - Part 1.mp4	42.0 MB
006 Implement the Soft Actor-Critic algorithm - Part 1_en.vtt	7.3 KB
007 Implement the Soft Actor-Critic algorithm - Part 2.mp4	69.9 MB
007 Implement the Soft Actor-Critic algorithm - Part 2_en.vtt	9.5 KB
008 Check the results.mp4	12.7 MB
008 Check the results_en.vtt	2.2 KB
/.../13 - Hindsight Experience Replay/
001 Hindsight Experience Replay (HER).mp4	17.9 MB
001 Hindsight Experience Replay (HER)_en.vtt	4.4 KB
002 Implement Hindsight Experience Replay (HER) - Part 1.mp4	35.6 MB
002 Implement Hindsight Experience Replay (HER) - Part 1_en.vtt	5.3 KB
003 Implement Hindsight Experience Replay (HER) - Part 2.mp4	22.7 MB
003 Implement Hindsight Experience Replay (HER) - Part 2_en.vtt	3.0 KB
004 Implement Hindsight Experience Replay (HER) - Part 3.mp4	77.3 MB
004 Implement Hindsight Experience Replay (HER) - Part 3_en.vtt	10.1 KB
005 Check the results.mp4	7.8 MB
005 Check the results_en.vtt	1.0 KB
/.../14 - Final steps/
001 Next steps.mp4	18.1 MB
001 Next steps_en.vtt	2.2 KB
002 Next steps.html	0.5 KB
/~Get Your Files Here !/
Bonus Resources.txt	0.4 KB
Total files 228