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[ FreeCourseWeb com ] Udemy - Advanced Reinforcement Learning - policy gradient methods
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01 - Introduction
001 Introduction.html
002 Reinforcement Learning series.html
003 Google Colab.mp4
003 Google Colab_en.vtt
004 Where to begin.html
02 - Refresher The Markov Decision Process (MDP)
001 Elements common to all control tasks.mp4
001 Elements common to all control tasks_en.vtt
002 The Markov decision process (MDP).mp4
002 The Markov decision process (MDP)_en.vtt
003 Types of Markov decision process.mp4
003 Types of Markov decision process_en.vtt
004 Trajectory vs episode.mp4
004 Trajectory vs episode_en.vtt
005 Reward vs Return.mp4
005 Reward vs Return_en.vtt
006 Discount factor.mp4
006 Discount factor_en.vtt
007 Policy.mp4
007 Policy_en.vtt
008 State values v(s) and action values q(s,a).mp4
008 State values v(s) and action values q(s,a)_en.vtt
009 Bellman equations.mp4
009 Bellman equations_en.vtt
010 Solving a Markov decision process.mp4
010 Solving a Markov decision process_en.vtt
03 - Refresher Monte Carlo methods
001 Monte Carlo methods.mp4
001 Monte Carlo methods_en.vtt
002 Solving control tasks with Monte Carlo methods.mp4
002 Solving control tasks with Monte Carlo methods_en.vtt
003 On-policy Monte Carlo control.mp4
003 On-policy Monte Carlo control_en.vtt
04 - Refresher Temporal difference methods
001 Temporal difference methods.mp4
001 Temporal difference methods_en.vtt
002 Solving control tasks with temporal difference methods.mp4
002 Solving control tasks with temporal difference methods_en.vtt
003 Monte Carlo vs temporal difference methods.mp4
003 Monte Carlo vs temporal difference methods_en.vtt
004 SARSA.mp4
004 SARSA_en.vtt
005 Q-Learning.mp4
005 Q-Learning_en.vtt
006 Advantages of temporal difference methods.mp4
006 Advantages of temporal difference methods_en.vtt
05 - Refresher N-step bootstrapping
001 N-step temporal difference methods.mp4
001 N-step temporal difference methods_en.vtt
002 Where do n-step methods fit.mp4
002 Where do n-step methods fit_en.vtt
003 Effect of changing n.mp4
003 Effect of changing n_en.vtt
06 - Refresher Brief introduction to Neural Networks
001 Function approximators.mp4
001 Function approximators_en.vtt
002 Artificial Neural Networks.mp4
002 Artificial Neural Networks_en.vtt
003 Artificial Neurons.mp4
003 Artificial Neurons_en.vtt
004 How to represent a Neural Network.mp4
004 How to represent a Neural Network_en.vtt
005 Stochastic Gradient Descent.mp4
005 Stochastic Gradient Descent_en.vtt
006 Neural Network optimization.mp4
006 Neural Network optimization_en.vtt
07 - Refresher REINFORCE
001 Policy gradient methods.mp4
001 Policy gradient methods_en.vtt
002 Representing policies using neural networks.mp4
002 Representing policies using neural networks_en.vtt
003 Policy performance.mp4
003 Policy performance_en.vtt
004 The policy gradient theorem.mp4
004 The policy gradient theorem_en.vtt
005 REINFORCE.mp4
005 REINFORCE_en.vtt
006 Parallel learning.mp4
006 Parallel learning_en.vtt
007 Entropy regularization.mp4
007 Entropy regularization_en.vtt
008 REINFORCE 2.mp4
008 REINFORCE 2_en.vtt
08 - PyTorch Lightning
001 PyTorch Lightning.mp4
001 PyTorch Lightning_en.vtt
002 Link to the code notebook.html
09 - REINFORCE for continuous control tasks
001 REINFORCE for continuous action spaces.html
10 - Advantage Actor Critic (A2C)
001 A2C.mp4
001 A2C_en.vtt
11 - Generalized Advantage Estimation (GAE)
001 Generalized Advantage Estimation.html
12 - Proximal Policy Optimization (PPO)
001 Proximal Policy Optimization.html
13 - Phasic PPO
001 Phasic PPO.html
Bonus Resources.txt
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