[ DevCourseWeb.com ] Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC番号下载_[ DevCourseWeb.com ] Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC番号下载_[ DevCourseWeb.com ] Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC下载

[ DevCourseWeb.com ] Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC

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种子名称： [ DevCourseWeb.com ] Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC
文件类型：视频
文件数目： 104个文件
文件大小： 2.42 GB
收录时间： 2023-12-29 02:50
已经下载： 3次
资源热度： 77
最近下载： 2024-5-29 13:34

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[ DevCourseWeb.com ] Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC.torrent

~Get Your Files Here !/01 - Introduction/001 Introduction.mp424.29MB

~Get Your Files Here !/01 - Introduction/003 Google Colab.mp45.77MB

~Get Your Files Here !/01 - Introduction/004 Where to begin.mp45.06MB

~Get Your Files Here !/02 - Refresher The Markov Decision Process (MDP)/001 Module Overview.mp42.61MB

~Get Your Files Here !/02 - Refresher The Markov Decision Process (MDP)/002 Elements common to all control tasks.mp438.74MB

~Get Your Files Here !/02 - Refresher The Markov Decision Process (MDP)/003 The Markov decision process (MDP).mp425.1MB

~Get Your Files Here !/02 - Refresher The Markov Decision Process (MDP)/004 Types of Markov decision process.mp48.68MB

~Get Your Files Here !/02 - Refresher The Markov Decision Process (MDP)/005 Trajectory vs episode.mp44.94MB

~Get Your Files Here !/02 - Refresher The Markov Decision Process (MDP)/006 Reward vs Return.mp45.28MB

~Get Your Files Here !/02 - Refresher The Markov Decision Process (MDP)/007 Discount factor.mp414.77MB

~Get Your Files Here !/02 - Refresher The Markov Decision Process (MDP)/008 Policy.mp47.41MB

~Get Your Files Here !/02 - Refresher The Markov Decision Process (MDP)/009 State values v(s) and action values q(s,a).mp44.28MB

~Get Your Files Here !/02 - Refresher The Markov Decision Process (MDP)/010 Bellman equations.mp412.41MB

~Get Your Files Here !/02 - Refresher The Markov Decision Process (MDP)/011 Solving a Markov decision process.mp414.14MB

~Get Your Files Here !/03 - Refresher Q-Learning/001 Module overview.mp41.49MB

~Get Your Files Here !/03 - Refresher Q-Learning/002 Temporal difference methods.mp412.62MB

~Get Your Files Here !/03 - Refresher Q-Learning/003 Solving control tasks with temporal difference methods.mp414.52MB

~Get Your Files Here !/03 - Refresher Q-Learning/004 Q-Learning.mp411.08MB

~Get Your Files Here !/03 - Refresher Q-Learning/005 Advantages of temporal difference methods.mp43.71MB

~Get Your Files Here !/04 - Refresher Brief introduction to Neural Networks/001 Module overview.mp41.78MB

~Get Your Files Here !/04 - Refresher Brief introduction to Neural Networks/002 Function approximators.mp436.32MB

~Get Your Files Here !/04 - Refresher Brief introduction to Neural Networks/003 Artificial Neural Networks.mp424.34MB

~Get Your Files Here !/04 - Refresher Brief introduction to Neural Networks/004 Artificial Neurons.mp425.64MB

~Get Your Files Here !/04 - Refresher Brief introduction to Neural Networks/005 How to represent a Neural Network.mp438.16MB

~Get Your Files Here !/04 - Refresher Brief introduction to Neural Networks/006 Stochastic Gradient Descent.mp449.87MB

~Get Your Files Here !/04 - Refresher Brief introduction to Neural Networks/007 Neural Network optimization.mp423.39MB

~Get Your Files Here !/05 - Refresher Deep Q-Learning/001 Module overview.mp41.27MB

~Get Your Files Here !/05 - Refresher Deep Q-Learning/002 Deep Q-Learning.mp416.17MB

~Get Your Files Here !/05 - Refresher Deep Q-Learning/003 Experience Replay.mp48.98MB

~Get Your Files Here !/05 - Refresher Deep Q-Learning/004 Target Network.mp416.57MB

~Get Your Files Here !/06 - PyTorch Lightning/001 PyTorch Lightning.mp432MB

~Get Your Files Here !/06 - PyTorch Lightning/003 Introduction to PyTorch Lightning.mp430.93MB

~Get Your Files Here !/06 - PyTorch Lightning/004 Create the Deep Q-Network.mp422.93MB

~Get Your Files Here !/06 - PyTorch Lightning/005 Create the policy.mp418.04MB

~Get Your Files Here !/06 - PyTorch Lightning/006 Create the replay buffer.mp422.98MB

~Get Your Files Here !/06 - PyTorch Lightning/007 Create the environment.mp432.22MB

~Get Your Files Here !/06 - PyTorch Lightning/008 Define the class for the Deep Q-Learning algorithm.mp454.54MB

~Get Your Files Here !/06 - PyTorch Lightning/009 Define the play_episode() function.mp429.1MB

~Get Your Files Here !/06 - PyTorch Lightning/010 Prepare the data loader and the optimizer.mp430.43MB

~Get Your Files Here !/06 - PyTorch Lightning/011 Define the train_step() method.mp449.77MB

~Get Your Files Here !/06 - PyTorch Lightning/012 Define the train_epoch_end() method.mp432.17MB

~Get Your Files Here !/06 - PyTorch Lightning/014 Train the Deep Q-Learning algorithm.mp435.03MB

~Get Your Files Here !/06 - PyTorch Lightning/015 Explore the resulting agent.mp420.26MB

~Get Your Files Here !/07 - Hyperparameter tuning with Optuna/001 Hyperparameter tuning with Optuna.mp432.42MB

~Get Your Files Here !/07 - Hyperparameter tuning with Optuna/003 Log average return.mp433.62MB

~Get Your Files Here !/07 - Hyperparameter tuning with Optuna/004 Define the objective function.mp429.84MB

~Get Your Files Here !/07 - Hyperparameter tuning with Optuna/005 Create and launch the hyperparameter tuning job.mp418.5MB

~Get Your Files Here !/07 - Hyperparameter tuning with Optuna/006 Explore the best trial.mp419.15MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/001 Continuous action spaces.mp429.64MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/002 The advantage function.mp413.42MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/003 Normalized Advantage Function (NAF).mp410.09MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/004 Normalized Advantage Function pseudocode.mp423.16MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/006 Hyperbolic tangent.mp44.68MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/007 Creating the (NAF) Deep Q-Network 1.mp441.41MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/008 Creating the (NAF) Deep Q-Network 2.mp415.01MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/009 Creating the (NAF) Deep Q-Network 3.mp45.38MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/010 Creating the (NAF) Deep Q-Network 4.mp447.88MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/011 Creating the policy.mp425.04MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/012 Create the environment.mp422.51MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/013 Polyak averaging.mp44.82MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/014 Implementing Polyak averaging.mp410.35MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/015 Create the (NAF) Deep Q-Learning algorithm.mp442.87MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/016 Implement the training step.mp413.27MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/017 Implement the end-of-epoch logic.mp412.49MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/018 Debugging and launching the algorithm.mp419.98MB

~Get Your Files Here !/08 - Deep Q-Learning for continuous action spaces (Normalized Advantage Function)/019 Checking the resulting agent.mp416.42MB

~Get Your Files Here !/09 - Refresher Policy gradient methods/001 Policy gradient methods.mp421.65MB

~Get Your Files Here !/09 - Refresher Policy gradient methods/002 Policy performance.mp48.52MB

~Get Your Files Here !/09 - Refresher Policy gradient methods/003 Representing policies using neural networks.mp427.76MB

~Get Your Files Here !/09 - Refresher Policy gradient methods/004 The policy gradient theorem.mp415.89MB

~Get Your Files Here !/09 - Refresher Policy gradient methods/005 Entropy Regularization.mp423.15MB

~Get Your Files Here !/10 - Deep Deterministic Policy Gradient (DDPG)/001 The Brax Physics engine.mp420.05MB

~Get Your Files Here !/10 - Deep Deterministic Policy Gradient (DDPG)/002 Deep Deterministic Policy Gradient (DDPG).mp432.33MB

~Get Your Files Here !/10 - Deep Deterministic Policy Gradient (DDPG)/003 DDPG pseudocode.mp420.92MB

~Get Your Files Here !/10 - Deep Deterministic Policy Gradient (DDPG)/005 Deep Deterministic Policy Gradient (DDPG).mp431.81MB

~Get Your Files Here !/10 - Deep Deterministic Policy Gradient (DDPG)/006 Create the gradient policy.mp443.44MB

~Get Your Files Here !/10 - Deep Deterministic Policy Gradient (DDPG)/007 Create the Deep Q-Network.mp422.77MB

~Get Your Files Here !/10 - Deep Deterministic Policy Gradient (DDPG)/008 Create the DDPG class.mp438.86MB

~Get Your Files Here !/10 - Deep Deterministic Policy Gradient (DDPG)/009 Define the play method.mp413.23MB

~Get Your Files Here !/10 - Deep Deterministic Policy Gradient (DDPG)/010 Setup the optimizers and dataloader.mp422.25MB

~Get Your Files Here !/10 - Deep Deterministic Policy Gradient (DDPG)/011 Define the training step.mp457.89MB

~Get Your Files Here !/10 - Deep Deterministic Policy Gradient (DDPG)/012 Launch the training process.mp434.2MB

~Get Your Files Here !/10 - Deep Deterministic Policy Gradient (DDPG)/013 Check the resulting agent.mp430.24MB

~Get Your Files Here !/11 - Twin Delayed DDPG (TD3)/001 Twin Delayed DDPG (TD3).mp433.96MB

~Get Your Files Here !/11 - Twin Delayed DDPG (TD3)/002 TD3 pseudocode.mp420.01MB

~Get Your Files Here !/11 - Twin Delayed DDPG (TD3)/004 Twin Delayed DDPG (TD3).mp419.95MB

~Get Your Files Here !/11 - Twin Delayed DDPG (TD3)/005 Clipped double Q-Learning.mp431.52MB

~Get Your Files Here !/11 - Twin Delayed DDPG (TD3)/006 Delayed policy updates.mp412.07MB

~Get Your Files Here !/11 - Twin Delayed DDPG (TD3)/007 Target policy smoothing.mp430.96MB

~Get Your Files Here !/11 - Twin Delayed DDPG (TD3)/008 Check the resulting agent.mp431.09MB

~Get Your Files Here !/12 - Soft Actor-Critic (SAC)/001 Soft Actor-Critic (SAC).mp423.96MB

~Get Your Files Here !/12 - Soft Actor-Critic (SAC)/002 SAC pseudocode.mp49.5MB

~Get Your Files Here !/12 - Soft Actor-Critic (SAC)/003 Create the robotics task.mp474.02MB

~Get Your Files Here !/12 - Soft Actor-Critic (SAC)/004 Create the Deep Q-Network.mp418.98MB

~Get Your Files Here !/12 - Soft Actor-Critic (SAC)/005 Create the gradient policy.mp453.76MB

~Get Your Files Here !/12 - Soft Actor-Critic (SAC)/006 Implement the Soft Actor-Critic algorithm - Part 1.mp440.06MB

~Get Your Files Here !/12 - Soft Actor-Critic (SAC)/007 Implement the Soft Actor-Critic algorithm - Part 2.mp466.71MB

~Get Your Files Here !/12 - Soft Actor-Critic (SAC)/008 Check the results.mp412.09MB

~Get Your Files Here !/13 - Hindsight Experience Replay/001 Hindsight Experience Replay (HER).mp417.07MB

~Get Your Files Here !/13 - Hindsight Experience Replay/002 Implement Hindsight Experience Replay (HER) - Part 1.mp433.98MB

~Get Your Files Here !/13 - Hindsight Experience Replay/003 Implement Hindsight Experience Replay (HER) - Part 2.mp421.66MB

~Get Your Files Here !/13 - Hindsight Experience Replay/004 Implement Hindsight Experience Replay (HER) - Part 3.mp473.74MB

~Get Your Files Here !/13 - Hindsight Experience Replay/005 Check the results.mp47.4MB

~Get Your Files Here !/14 - Final steps/001 Next steps.mp417.27MB