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fileartificial-intelligence-reinforcement-learning-in-python

aricial intelligence reinforcement learning python
  • MP402 Return of the Multi-Armed Bandit\\/007 Updating a Sample Mean.mp42.17MB
  • MP404 rkov Decision Proccesses\\/031 MDP Summary.mp42.41MB
  • MP407 Temporal Difference Learning\\/051 Temporal Difference Intro.mp42.72MB
  • MP402 Return of the Multi-Armed Bandit\\/006 Epsilon-Greedy.mp42.78MB
  • MP408 Approxition Methods\\/062 Monte Carlo Prediction with Approximation.mp42.84MB
  • MP405 Dynamic Programming\\/036 Policy Iteration.mp43.13MB
  • MP404 rkov Decision Proccesses\\/025 Gridworld.mp43.36MB
  • MP407 Temporal Difference Learning\\/058 TD Sumry.mp43.94MB
  • MP409 Appendix\\/069 Where to get discount coupons and FREE deep learning terial.mp44.02MB
  • MP403 Build an Intelligent Tic-Tac-Toe Agent\\/016 Notes on Assigning Rewards.mp44.22MB
  • MP403 Build an Intelligent Tic-Tac-Toe Agent\\/019 Tic Tac Toe Code Representing States.mp44.42MB
  • MP401 Introduction and Outline\\/003 Where to get the Code.mp44.45MB
  • MP405 Dynamic Programming\\/035 Policy Improvement.mp44.53MB
  • MP406 Monte Carlo\\/048 Monte Carlo Control without Exploring Starts.mp44.62MB
  • MP408 Approxition Methods\\/065 Semi-Gradient SARSA.mp44.70MB
  • MP405 Dynamic Programming\\/032 Intro to Dynamic Programming and Iterative Policy Evaluation.mp44.83MB
  • MP407 Temporal Difference Learning\\/056 Q Learning.mp44.84MB
  • MP405 Dynamic Programming\\/040 Value Iteration in Code.mp44.89MB
  • MP406 Monte Carlo\\/042 Monte Carlo Intro.mp44.97MB
  • MP403 Build an Intelligent Tic-Tac-Toe Agent\\/018 Tic Tac Toe Code Outline.mp45.03MB
  • MP402 Return of the Multi-Armed Bandit\\/009 Optimistic Initial Values.mp45.12MB
  • MP404 rkov Decision Proccesses\\/028 Future Rewards.mp45.17MB
  • MP407 Temporal Difference Learning\\/053 TD0 Prediction in Code.mp45.32MB
  • MP407 Temporal Difference Learning\\/057 Q Learning in Code.mp45.42MB
  • MP406 Monte Carlo\\/050 Monte Carlo Sumry.mp45.71MB
  • MP407 Temporal Difference Learning\\/052 TD0 Prediction.mp45.82MB
  • MP403 Build an Intelligent Tic-Tac-Toe Agent\\/014 Naive Solution to Tic-Tac-Toe.mp46.11MB
  • MP405 Dynamic Programming\\/039 Value Iteration.mp46.18MB
  • MP408 Approxition Methods\\/061 Features.mp46.24MB
  • MP404 rkov Decision Proccesses\\/030 Optimal Policy and Optimal Value Function.mp46.31MB
  • MP408 Approxition Methods\\/059 Approximation Intro.mp46.46MB
  • MP408 Approxition Methods\\/060 Linear Models for Reinforcement Learning.mp46.46MB
  • MP402 Return of the Multi-Armed Bandit\\/005 Problem Setup and The Explore-Exploit Dilem.mp46.47MB
  • MP408 Approxition Methods\\/063 Monte Carlo Prediction with Approximation in Code.mp46.56MB
  • MP404 rkov Decision Proccesses\\/027 Defining and Formalizing the MDP.mp46.64MB
  • MP404 rkov Decision Proccesses\\/029 Value Functions.mp47.08MB
  • MP404 rkov Decision Proccesses\\/026 The Markov Property.mp47.18MB
  • MP402 Return of the Multi-Armed Bandit\\/013 Nonstationary Bandits.mp47.48MB
  • MP405 Dynamic Programming\\/037 Policy Iteration in Code.mp47.62MB
  • MP406 Monte Carlo\\/045 Policy Evaluation in Windy Gridworld.mp47.81MB
  • MP406 Monte Carlo\\/044 Monte Carlo Policy Evaluation in Code.mp47.91MB
  • MP402 Return of the Multi-Armed Bandit\\/008 Comparing Different Epsilons.mp48.01MB
  • MP406 Monte Carlo\\/049 Monte Carlo Control without Exploring Starts in Code.mp48.05MB
  • MP407 Temporal Difference Learning\\/054 SARSA.mp48.20MB
  • MP402 Return of the Multi-Armed Bandit\\/010 UCB1.mp48.23MB
  • MP403 Build an Intelligent Tic-Tac-Toe Agent\\/024 Tic Tac Toe Sumry.mp48.31MB
  • MP405 Dynamic Programming\\/041 Dynamic Programming Sumry.mp48.31MB
  • MP408 Approxition Methods\\/0 TD0 Semi-Gradient Prediction.mp48.35MB
  • MP406 Monte Carlo\\/043 Monte Carlo Policy Evaluation.mp48.75MB
  • MP407 Temporal Difference Learning\\/055 SARSA in Code.mp48.82MB
  • MP403 Build an Intelligent Tic-Tac-Toe Agent\\/022 Tic Tac Toe Code The Agent.mp49.01MB
  • MP405 Dynamic Programming\\/038 Policy Iteration in Windy Gridworld.mp49.10MB
  • MP406 Monte Carlo\\/046 Monte Carlo Control.mp49.26MB
  • MP403 Build an Intelligent Tic-Tac-Toe Agent\\/023 Tic Tac Toe Code in Loop and Demo.mp49.44MB
  • MP401 Introduction and Outline\\/004 Strategy for Passing the Course.mp49.47MB
  • MP403 Build an Intelligent Tic-Tac-Toe Agent\\/020 Tic Tac Toe Code Enumerating States Recursively.mp49.79MB
  • MP403 Build an Intelligent Tic-Tac-Toe Agent\\/021 Tic Tac Toe Code The Environment.mp410.05MB
  • MP401 Introduction and Outline\\/001 Introduction and outline.mp410.10MB
  • MP406 Monte Carlo\\/047 Monte Carlo Control in Code.mp410.17MB
  • MP402 Return of the Multi-Armed Bandit\\/012 Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp410.57MB
  • MP408 Approxition Methods\\/066 Semi-Gradient SARSA in Code.mp410.61MB
  • MP405 Dynamic Programming\\/033 Gridworld in Code.mp411.46MB
  • MP405 Dynamic Programming\\/034 Iterative Policy Evaluation in Code.mp412.06MB
  • MP403 Build an Intelligent Tic-Tac-Toe Agent\\/015 Components of a Reinforcement Learning System.mp412.71MB
  • MP408 Approxition Methods\\/067 Course Summary and Next Steps.mp413.24MB
  • MP402 Return of the Multi-Armed Bandit\\/011 Bayesian Thompson Sampling.mp415.23MB
  • MP401 Introduction and Outline\\/002 What is Reinforcement Learning.mp421.94MB
  • MP403 Build an Intelligent Tic-Tac-Toe Agent\\/017 The Value Function and Your First Reinforcement Learning Algorithm.mp426.13MB
  • MP409 Appendix\\/068 How to install Numpy Scipy tplotlib Pandas IPython Theano and TensorFlow.mp443.92MB
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