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[ DevCourseWeb com ] Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC
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01 - Introduction
001 Introduction.mp4
001 Introduction_en.vtt
002 Reinforcement Learning series.html
003 Google Colab.mp4
003 Google Colab_en.vtt
004 Where to begin.mp4
004 Where to begin_en.vtt
external-assets-links.txt
02 - Refresher The Markov Decision Process (MDP)
001 Module Overview.mp4
001 Module Overview_en.vtt
002 Elements common to all control tasks.mp4
002 Elements common to all control tasks_en.vtt
003 The Markov decision process (MDP).mp4
003 The Markov decision process (MDP)_en.vtt
004 Types of Markov decision process.mp4
004 Types of Markov decision process_en.vtt
005 Trajectory vs episode.mp4
005 Trajectory vs episode_en.vtt
006 Reward vs Return.mp4
006 Reward vs Return_en.vtt
007 Discount factor.mp4
007 Discount factor_en.vtt
008 Policy.mp4
008 Policy_en.vtt
009 State values v(s) and action values q(s,a).mp4
009 State values v(s) and action values q(s,a)_en.vtt
010 Bellman equations.mp4
010 Bellman equations_en.vtt
011 Solving a Markov decision process.mp4
011 Solving a Markov decision process_en.vtt
external-assets-links.txt
03 - Refresher Q-Learning
001 Module overview.mp4
001 Module overview_en.vtt
002 Temporal difference methods.mp4
002 Temporal difference methods_en.vtt
003 Solving control tasks with temporal difference methods.mp4
003 Solving control tasks with temporal difference methods_en.vtt
004 Q-Learning.mp4
004 Q-Learning_en.vtt
005 Advantages of temporal difference methods.mp4
005 Advantages of temporal difference methods_en.vtt
external-assets-links.txt
04 - Refresher Brief introduction to Neural Networks
001 Module overview.mp4
001 Module overview_en.vtt
002 Function approximators.mp4
002 Function approximators_en.vtt
003 Artificial Neural Networks.mp4
003 Artificial Neural Networks_en.vtt
004 Artificial Neurons.mp4
004 Artificial Neurons_en.vtt
005 How to represent a Neural Network.mp4
005 How to represent a Neural Network_en.vtt
006 Stochastic Gradient Descent.mp4
006 Stochastic Gradient Descent_en.vtt
007 Neural Network optimization.mp4
007 Neural Network optimization_en.vtt
external-assets-links.txt
05 - Refresher Deep Q-Learning
001 Module overview.mp4
001 Module overview_en.vtt
002 Deep Q-Learning.mp4
002 Deep Q-Learning_en.vtt
003 Experience Replay.mp4
003 Experience Replay_en.vtt
004 Target Network.mp4
004 Target Network_en.vtt
external-assets-links.txt
06 - PyTorch Lightning
001 PyTorch Lightning.mp4
001 PyTorch Lightning_en.vtt
002 Link to the code notebook.html
003 Introduction to PyTorch Lightning.mp4
003 Introduction to PyTorch Lightning_en.vtt
004 Create the Deep Q-Network.mp4
004 Create the Deep Q-Network_en.vtt
005 Create the policy.mp4
005 Create the policy_en.vtt
006 Create the replay buffer.mp4
006 Create the replay buffer_en.vtt
007 Create the environment.mp4
007 Create the environment_en.vtt
008 Define the class for the Deep Q-Learning algorithm.mp4
008 Define the class for the Deep Q-Learning algorithm_en.vtt
009 Define the play_episode() function.mp4
009 Define the play_episode() function_en.vtt
010 Prepare the data loader and the optimizer.mp4
010 Prepare the data loader and the optimizer_en.vtt
011 Define the train_step() method.mp4
011 Define the train_step() method_en.vtt
012 Define the train_epoch_end() method.mp4
012 Define the train_epoch_end() method_en.vtt
013 [Important] Lecture correction.html
014 Train the Deep Q-Learning algorithm.mp4
014 Train the Deep Q-Learning algorithm_en.vtt
015 Explore the resulting agent.mp4
015 Explore the resulting agent_en.vtt
external-assets-links.txt
07 - Hyperparameter tuning with Optuna
001 Hyperparameter tuning with Optuna.mp4
001 Hyperparameter tuning with Optuna_en.vtt
002 Link to the code notebook.html
003 Log average return.mp4
003 Log average return_en.vtt
004 Define the objective function.mp4
004 Define the objective function_en.vtt
005 Create and launch the hyperparameter tuning job.mp4
005 Create and launch the hyperparameter tuning job_en.vtt
006 Explore the best trial.mp4
006 Explore the best trial_en.vtt
external-assets-links.txt
08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)
001 Continuous action spaces.mp4
001 Continuous action spaces_en.vtt
002 The advantage function.mp4
002 The advantage function_en.vtt
003 Normalized Advantage Function (NAF).mp4
003 Normalized Advantage Function (NAF)_en.vtt
004 Normalized Advantage Function pseudocode.mp4
004 Normalized Advantage Function pseudocode_en.vtt
005 Link to the code notebook.html
006 Hyperbolic tangent.mp4
006 Hyperbolic tangent_en.vtt
007 Creating the (NAF) Deep Q-Network 1.mp4
007 Creating the (NAF) Deep Q-Network 1_en.vtt
008 Creating the (NAF) Deep Q-Network 2.mp4
008 Creating the (NAF) Deep Q-Network 2_en.vtt
009 Creating the (NAF) Deep Q-Network 3.mp4
009 Creating the (NAF) Deep Q-Network 3_en.vtt
010 Creating the (NAF) Deep Q-Network 4.mp4
010 Creating the (NAF) Deep Q-Network 4_en.vtt
011 Creating the policy.mp4
011 Creating the policy_en.vtt
012 Create the environment.mp4
012 Create the environment_en.vtt
013 Polyak averaging.mp4
013 Polyak averaging_en.vtt
014 Implementing Polyak averaging.mp4
014 Implementing Polyak averaging_en.vtt
015 Create the (NAF) Deep Q-Learning algorithm.mp4
015 Create the (NAF) Deep Q-Learning algorithm_en.vtt
016 Implement the training step.mp4
016 Implement the training step_en.vtt
017 Implement the end-of-epoch logic.mp4
017 Implement the end-of-epoch logic_en.vtt
018 Debugging and launching the algorithm.mp4
018 Debugging and launching the algorithm_en.vtt
019 Checking the resulting agent.mp4
019 Checking the resulting agent_en.vtt
external-assets-links.txt
09 - Refresher Policy gradient methods
001 Policy gradient methods.mp4
001 Policy gradient methods_en.vtt
002 Policy performance.mp4
002 Policy performance_en.vtt
003 Representing policies using neural networks.mp4
003 Representing policies using neural networks_en.vtt
004 The policy gradient theorem.mp4
004 The policy gradient theorem_en.vtt
005 Entropy Regularization.mp4
005 Entropy Regularization_en.vtt
10 - Deep Deterministic Policy Gradient (DDPG)
001 The Brax Physics engine.mp4
001 The Brax Physics engine_en.vtt
002 Deep Deterministic Policy Gradient (DDPG).mp4
002 Deep Deterministic Policy Gradient (DDPG)_en.vtt
003 DDPG pseudocode.mp4
003 DDPG pseudocode_en.vtt
004 Link to the code notebook.html
005 Deep Deterministic Policy Gradient (DDPG).mp4
005 Deep Deterministic Policy Gradient (DDPG)_en.vtt
006 Create the gradient policy.mp4
006 Create the gradient policy_en.vtt
007 Create the Deep Q-Network.mp4
007 Create the Deep Q-Network_en.vtt
008 Create the DDPG class.mp4
008 Create the DDPG class_en.vtt
009 Define the play method.mp4
009 Define the play method_en.vtt
010 Setup the optimizers and dataloader.mp4
010 Setup the optimizers and dataloader_en.vtt
011 Define the training step.mp4
011 Define the training step_en.vtt
012 Launch the training process.mp4
012 Launch the training process_en.vtt
013 Check the resulting agent.mp4
013 Check the resulting agent_en.vtt
external-assets-links.txt
11 - Twin Delayed DDPG (TD3)
001 Twin Delayed DDPG (TD3).mp4
001 Twin Delayed DDPG (TD3)_en.vtt
002 TD3 pseudocode.mp4
002 TD3 pseudocode_en.vtt
003 Link to code notebook.html
004 Twin Delayed DDPG (TD3).mp4
004 Twin Delayed DDPG (TD3)_en.vtt
005 Clipped double Q-Learning.mp4
005 Clipped double Q-Learning_en.vtt
006 Delayed policy updates.mp4
006 Delayed policy updates_en.vtt
007 Target policy smoothing.mp4
007 Target policy smoothing_en.vtt
008 Check the resulting agent.mp4
008 Check the resulting agent_en.vtt
external-assets-links.txt
12 - Soft Actor-Critic (SAC)
001 Soft Actor-Critic (SAC).mp4
001 Soft Actor-Critic (SAC)_en.vtt
002 SAC pseudocode.mp4
002 SAC pseudocode_en.vtt
003 Create the robotics task.mp4
003 Create the robotics task_en.vtt
004 Create the Deep Q-Network.mp4
004 Create the Deep Q-Network_en.vtt
005 Create the gradient policy.mp4
005 Create the gradient policy_en.vtt
006 Implement the Soft Actor-Critic algorithm - Part 1.mp4
006 Implement the Soft Actor-Critic algorithm - Part 1_en.vtt
007 Implement the Soft Actor-Critic algorithm - Part 2.mp4
007 Implement the Soft Actor-Critic algorithm - Part 2_en.vtt
008 Check the results.mp4
008 Check the results_en.vtt
13 - Hindsight Experience Replay
001 Hindsight Experience Replay (HER).mp4
001 Hindsight Experience Replay (HER)_en.vtt
002 Implement Hindsight Experience Replay (HER) - Part 1.mp4
002 Implement Hindsight Experience Replay (HER) - Part 1_en.vtt
003 Implement Hindsight Experience Replay (HER) - Part 2.mp4
003 Implement Hindsight Experience Replay (HER) - Part 2_en.vtt
004 Implement Hindsight Experience Replay (HER) - Part 3.mp4
004 Implement Hindsight Experience Replay (HER) - Part 3_en.vtt
005 Check the results.mp4
005 Check the results_en.vtt
14 - Final steps
001 Next steps.mp4
001 Next steps_en.vtt
002 Next steps.html
Bonus Resources.txt
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