artificial-intelligence-reinforcement-learning-in-python

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种子哈希：508d18d0f2e7ae69a116b936bbaac4252d92d3db
文档大小：580.1 MB
文档个数：69个文档
下载次数：481次
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收录时间：2017-06-03
最近下载：2025-04-13
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文档列表

02 Return of the Multi-Armed Bandit/007 Updating a Sample Mean.mp4 2.3 MB
04 Markov Decision Proccesses/031 MDP Summary.mp4 2.5 MB
07 Temporal Difference Learning/051 Temporal Difference Intro.mp4 2.9 MB
02 Return of the Multi-Armed Bandit/006 Epsilon-Greedy.mp4 2.9 MB
08 Approximation Methods/062 Monte Carlo Prediction with Approximation.mp4 3.0 MB
05 Dynamic Programming/036 Policy Iteration.mp4 3.3 MB
04 Markov Decision Proccesses/025 Gridworld.mp4 3.5 MB
07 Temporal Difference Learning/058 TD Summary.mp4 4.1 MB
09 Appendix/069 Where to get discount coupons and FREE deep learning material.mp4 4.2 MB
03 Build an Intelligent Tic-Tac-Toe Agent/016 Notes on Assigning Rewards.mp4 4.4 MB
03 Build an Intelligent Tic-Tac-Toe Agent/019 Tic Tac Toe Code Representing States.mp4 4.6 MB
01 Introduction and Outline/003 Where to get the Code.mp4 4.7 MB
05 Dynamic Programming/035 Policy Improvement.mp4 4.8 MB
06 Monte Carlo/048 Monte Carlo Control without Exploring Starts.mp4 4.8 MB
08 Approximation Methods/065 Semi-Gradient SARSA.mp4 4.9 MB
05 Dynamic Programming/032 Intro to Dynamic Programming and Iterative Policy Evaluation.mp4 5.1 MB
07 Temporal Difference Learning/056 Q Learning.mp4 5.1 MB
05 Dynamic Programming/040 Value Iteration in Code.mp4 5.1 MB
06 Monte Carlo/042 Monte Carlo Intro.mp4 5.2 MB
03 Build an Intelligent Tic-Tac-Toe Agent/018 Tic Tac Toe Code Outline.mp4 5.3 MB
02 Return of the Multi-Armed Bandit/009 Optimistic Initial Values.mp4 5.4 MB
04 Markov Decision Proccesses/028 Future Rewards.mp4 5.4 MB
07 Temporal Difference Learning/053 TD0 Prediction in Code.mp4 5.6 MB
07 Temporal Difference Learning/057 Q Learning in Code.mp4 5.7 MB
06 Monte Carlo/050 Monte Carlo Summary.mp4 6.0 MB
07 Temporal Difference Learning/052 TD0 Prediction.mp4 6.1 MB
03 Build an Intelligent Tic-Tac-Toe Agent/014 Naive Solution to Tic-Tac-Toe.mp4 6.4 MB
05 Dynamic Programming/039 Value Iteration.mp4 6.5 MB
08 Approximation Methods/061 Features.mp4 6.5 MB
04 Markov Decision Proccesses/030 Optimal Policy and Optimal Value Function.mp4 6.6 MB
08 Approximation Methods/059 Approximation Intro.mp4 6.8 MB
08 Approximation Methods/060 Linear Models for Reinforcement Learning.mp4 6.8 MB
02 Return of the Multi-Armed Bandit/005 Problem Setup and The Explore-Exploit Dilemma.mp4 6.8 MB
08 Approximation Methods/063 Monte Carlo Prediction with Approximation in Code.mp4 6.9 MB
04 Markov Decision Proccesses/027 Defining and Formalizing the MDP.mp4 7.0 MB
04 Markov Decision Proccesses/029 Value Functions.mp4 7.4 MB
04 Markov Decision Proccesses/026 The Markov Property.mp4 7.5 MB
02 Return of the Multi-Armed Bandit/013 Nonstationary Bandits.mp4 7.8 MB
05 Dynamic Programming/037 Policy Iteration in Code.mp4 8.0 MB
06 Monte Carlo/045 Policy Evaluation in Windy Gridworld.mp4 8.2 MB
06 Monte Carlo/044 Monte Carlo Policy Evaluation in Code.mp4 8.3 MB
02 Return of the Multi-Armed Bandit/008 Comparing Different Epsilons.mp4 8.4 MB
06 Monte Carlo/049 Monte Carlo Control without Exploring Starts in Code.mp4 8.4 MB
07 Temporal Difference Learning/054 SARSA.mp4 8.6 MB
02 Return of the Multi-Armed Bandit/010 UCB1.mp4 8.6 MB
03 Build an Intelligent Tic-Tac-Toe Agent/024 Tic Tac Toe Summary.mp4 8.7 MB
05 Dynamic Programming/041 Dynamic Programming Summary.mp4 8.7 MB
08 Approximation Methods/064 TD0 Semi-Gradient Prediction.mp4 8.8 MB
06 Monte Carlo/043 Monte Carlo Policy Evaluation.mp4 9.2 MB
07 Temporal Difference Learning/055 SARSA in Code.mp4 9.2 MB
03 Build an Intelligent Tic-Tac-Toe Agent/022 Tic Tac Toe Code The Agent.mp4 9.4 MB
05 Dynamic Programming/038 Policy Iteration in Windy Gridworld.mp4 9.5 MB
06 Monte Carlo/046 Monte Carlo Control.mp4 9.7 MB
03 Build an Intelligent Tic-Tac-Toe Agent/023 Tic Tac Toe Code Main Loop and Demo.mp4 9.9 MB
01 Introduction and Outline/004 Strategy for Passing the Course.mp4 9.9 MB
03 Build an Intelligent Tic-Tac-Toe Agent/020 Tic Tac Toe Code Enumerating States Recursively.mp4 10.3 MB
03 Build an Intelligent Tic-Tac-Toe Agent/021 Tic Tac Toe Code The Environment.mp4 10.5 MB
01 Introduction and Outline/001 Introduction and outline.mp4 10.6 MB
06 Monte Carlo/047 Monte Carlo Control in Code.mp4 10.7 MB
02 Return of the Multi-Armed Bandit/012 Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4 11.1 MB
08 Approximation Methods/066 Semi-Gradient SARSA in Code.mp4 11.1 MB
05 Dynamic Programming/033 Gridworld in Code.mp4 12.0 MB
05 Dynamic Programming/034 Iterative Policy Evaluation in Code.mp4 12.6 MB
03 Build an Intelligent Tic-Tac-Toe Agent/015 Components of a Reinforcement Learning System.mp4 13.3 MB
08 Approximation Methods/067 Course Summary and Next Steps.mp4 13.9 MB
02 Return of the Multi-Armed Bandit/011 Bayesian Thompson Sampling.mp4 16.0 MB
01 Introduction and Outline/002 What is Reinforcement Learning.mp4 23.0 MB
03 Build an Intelligent Tic-Tac-Toe Agent/017 The Value Function and Your First Reinforcement Learning Algorithm.mp4 27.4 MB
09 Appendix/068 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp4 46.0 MB

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