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02 Return of the Multi-Armed Bandit/007 Updating a Sample Mean.mp4 2.17 MB
04 Markov Decision Proccesses/031 MDP Summary.mp4 2.41 MB
07 Temporal Difference Learning/051 Temporal Difference Intro.mp4 2.72 MB
02 Return of the Multi-Armed Bandit/006 Epsilon-Greedy.mp4 2.78 MB
08 Approximation Methods/062 Monte Carlo Prediction with Approximation.mp4 2.84 MB
05 Dynamic Programming/036 Policy Iteration.mp4 3.13 MB
04 Markov Decision Proccesses/025 Gridworld.mp4 3.36 MB
07 Temporal Difference Learning/058 TD Summary.mp4 3.94 MB
09 Appendix/069 Where to get discount coupons and FREE deep learning material.mp4 4.02 MB
03 Build an Intelligent Tic-Tac-Toe Agent/016 Notes on Assigning Rewards.mp4 4.22 MB
03 Build an Intelligent Tic-Tac-Toe Agent/019 Tic Tac Toe Code Representing States.mp4 4.42 MB
01 Introduction and Outline/003 Where to get the Code.mp4 4.45 MB
05 Dynamic Programming/035 Policy Improvement.mp4 4.53 MB
06 Monte Carlo/048 Monte Carlo Control without Exploring Starts.mp4 4.62 MB
08 Approximation Methods/065 Semi-Gradient SARSA.mp4 4.7 MB
05 Dynamic Programming/032 Intro to Dynamic Programming and Iterative Policy Evaluation.mp4 4.83 MB
07 Temporal Difference Learning/056 Q Learning.mp4 4.84 MB
05 Dynamic Programming/040 Value Iteration in Code.mp4 4.89 MB
06 Monte Carlo/042 Monte Carlo Intro.mp4 4.97 MB
03 Build an Intelligent Tic-Tac-Toe Agent/018 Tic Tac Toe Code Outline.mp4 5.03 MB
02 Return of the Multi-Armed Bandit/009 Optimistic Initial Values.mp4 5.12 MB
04 Markov Decision Proccesses/028 Future Rewards.mp4 5.17 MB
07 Temporal Difference Learning/053 TD0 Prediction in Code.mp4 5.32 MB
07 Temporal Difference Learning/057 Q Learning in Code.mp4 5.42 MB
06 Monte Carlo/050 Monte Carlo Summary.mp4 5.71 MB
07 Temporal Difference Learning/052 TD0 Prediction.mp4 5.82 MB
03 Build an Intelligent Tic-Tac-Toe Agent/014 Naive Solution to Tic-Tac-Toe.mp4 6.11 MB
05 Dynamic Programming/039 Value Iteration.mp4 6.18 MB
08 Approximation Methods/061 Features.mp4 6.24 MB
04 Markov Decision Proccesses/030 Optimal Policy and Optimal Value Function.mp4 6.31 MB
08 Approximation Methods/059 Approximation Intro.mp4 6.46 MB
08 Approximation Methods/060 Linear Models for Reinforcement Learning.mp4 6.46 MB
02 Return of the Multi-Armed Bandit/005 Problem Setup and The Explore-Exploit Dilemma.mp4 6.47 MB
08 Approximation Methods/063 Monte Carlo Prediction with Approximation in Code.mp4 6.56 MB
04 Markov Decision Proccesses/027 Defining and Formalizing the MDP.mp4 6.64 MB
04 Markov Decision Proccesses/029 Value Functions.mp4 7.08 MB
04 Markov Decision Proccesses/026 The Markov Property.mp4 7.18 MB
02 Return of the Multi-Armed Bandit/013 Nonstationary Bandits.mp4 7.48 MB
05 Dynamic Programming/037 Policy Iteration in Code.mp4 7.62 MB
06 Monte Carlo/045 Policy Evaluation in Windy Gridworld.mp4 7.81 MB
06 Monte Carlo/044 Monte Carlo Policy Evaluation in Code.mp4 7.91 MB
02 Return of the Multi-Armed Bandit/008 Comparing Different Epsilons.mp4 8.01 MB
06 Monte Carlo/049 Monte Carlo Control without Exploring Starts in Code.mp4 8.05 MB
07 Temporal Difference Learning/054 SARSA.mp4 8.2 MB
02 Return of the Multi-Armed Bandit/010 UCB1.mp4 8.23 MB
03 Build an Intelligent Tic-Tac-Toe Agent/024 Tic Tac Toe Summary.mp4 8.31 MB
05 Dynamic Programming/041 Dynamic Programming Summary.mp4 8.31 MB
08 Approximation Methods/064 TD0 Semi-Gradient Prediction.mp4 8.35 MB
06 Monte Carlo/043 Monte Carlo Policy Evaluation.mp4 8.75 MB
07 Temporal Difference Learning/055 SARSA in Code.mp4 8.82 MB
03 Build an Intelligent Tic-Tac-Toe Agent/022 Tic Tac Toe Code The Agent.mp4 9.01 MB
05 Dynamic Programming/038 Policy Iteration in Windy Gridworld.mp4 9.1 MB
06 Monte Carlo/046 Monte Carlo Control.mp4 9.26 MB
03 Build an Intelligent Tic-Tac-Toe Agent/023 Tic Tac Toe Code Main Loop and Demo.mp4 9.44 MB
01 Introduction and Outline/004 Strategy for Passing the Course.mp4 9.47 MB
03 Build an Intelligent Tic-Tac-Toe Agent/020 Tic Tac Toe Code Enumerating States Recursively.mp4 9.79 MB
03 Build an Intelligent Tic-Tac-Toe Agent/021 Tic Tac Toe Code The Environment.mp4 10.05 MB
01 Introduction and Outline/001 Introduction and outline.mp4 10.1 MB
06 Monte Carlo/047 Monte Carlo Control in Code.mp4 10.17 MB
02 Return of the Multi-Armed Bandit/012 Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4 10.57 MB
08 Approximation Methods/066 Semi-Gradient SARSA in Code.mp4 10.61 MB
05 Dynamic Programming/033 Gridworld in Code.mp4 11.46 MB
05 Dynamic Programming/034 Iterative Policy Evaluation in Code.mp4 12.06 MB
03 Build an Intelligent Tic-Tac-Toe Agent/015 Components of a Reinforcement Learning System.mp4 12.71 MB
08 Approximation Methods/067 Course Summary and Next Steps.mp4 13.24 MB
02 Return of the Multi-Armed Bandit/011 Bayesian Thompson Sampling.mp4 15.23 MB
01 Introduction and Outline/002 What is Reinforcement Learning.mp4 21.94 MB
03 Build an Intelligent Tic-Tac-Toe Agent/017 The Value Function and Your First Reinforcement Learning Algorithm.mp4 26.13 MB
09 Appendix/068 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp4 43.92 MB
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