Udemy - Artificial Intelligence: Reinforcement Learning in Python [Giga Course]

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Udemy - Artificial Intelligence: Reinforcement Learning in Python [Giga Course]

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Udemy - Artificial Intelligence: Reinforcement Learning in Python [Giga Course]
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1. Approximation Methods Section Introduction-en_US.srt
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1. Approximation Methods Section Introduction.mp4
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1. Beginners, halt! Stop here if you skipped ahead-en_US.srt
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1. Beginners, halt! Stop here if you skipped ahead.mp4
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1. Dynamic Programming Section Introduction-en_US.srt
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1. Dynamic Programming Section Introduction.mp4
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1. How to Code by Yourself (part 1)-en_US.srt
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1. How to Code by Yourself (part 1).mp4
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1. How to Succeed in this Course (Long Version)-en_US.srt
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1. How to Succeed in this Course (Long Version).mp4
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1. Introduction-en_US.srt
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1. Introduction.mp4
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1. MDP Section Introduction-en_US.srt
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1. MDP Section Introduction.mp4
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1. Monte Carlo Intro-en_US.srt
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1. Monte Carlo Intro.mp4
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1. Section Introduction The Explore-Exploit Dilemma-en_US.srt
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1. Section Introduction The Explore-Exploit Dilemma.mp4
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1. Temporal Difference Introduction-en_US.srt
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1. Temporal Difference Introduction.mp4
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1. This Course vs. RL Book What's the Difference-en_US.srt
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1. This Course vs. RL Book What's the Difference.mp4
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1. What is Reinforcement Learning-en_US.srt
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1. What is Reinforcement Learning.mp4
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1. What is the Appendix-en_US.srt
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1. What is the Appendix.mp4
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1. Windows-Focused Environment Setup 2018-en_US.srt
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1. Windows-Focused Environment Setup 2018.mp4
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10. Approximation Methods Exercise-en_US.srt
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10. Approximation Methods Exercise.mp4
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10. Optimistic Initial Values Beginner's Exercise Prompt-en_US.srt
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10. Optimistic Initial Values Beginner's Exercise Prompt.mp4
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10. Policy Iteration in Code-en_US.srt
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10. Policy Iteration in Code.mp4
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10. Stock Trading Project Discussion-en_US.srt
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10. Stock Trading Project Discussion.mp4
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10. The Bellman Equation (pt 3)-en_US.srt
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10. The Bellman Equation (pt 3).mp4
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11. Approximation Methods Section Summary-en_US.srt
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11. Approximation Methods Section Summary.mp4
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11. Bellman Examples-en_US.srt
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11. Bellman Examples.mp4
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11. Optimistic Initial Values Code-en_US.srt
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11. Optimistic Initial Values Code.mp4
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11. Policy Iteration in Windy Gridworld-en_US.srt
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11. Policy Iteration in Windy Gridworld.mp4
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12. Optimal Policy and Optimal Value Function (pt 1)-en_US.srt
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12. Optimal Policy and Optimal Value Function (pt 1).mp4
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12. UCB1 Theory-en_US.srt
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12. UCB1 Theory.mp4
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12. Value Iteration-en_US.srt
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12. Value Iteration.mp4
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13. Optimal Policy and Optimal Value Function (pt 2)-en_US.srt
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13. Optimal Policy and Optimal Value Function (pt 2).mp4
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13. UCB1 Beginner's Exercise Prompt-en_US.srt
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13. UCB1 Beginner's Exercise Prompt.mp4
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13. Value Iteration in Code-en_US.srt
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13. Value Iteration in Code.mp4
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14. Dynamic Programming Summary-en_US.srt
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14. Dynamic Programming Summary.mp4
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14. MDP Summary-en_US.srt
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14. MDP Summary.mp4
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14. UCB1 Code-en_US.srt
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14. UCB1 Code.mp4
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15. Bayesian Bandits Thompson Sampling Theory (pt 1)-en_US.srt
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15. Bayesian Bandits Thompson Sampling Theory (pt 1).mp4
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16. Bayesian Bandits Thompson Sampling Theory (pt 2)-en_US.srt
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16. Bayesian Bandits Thompson Sampling Theory (pt 2).mp4
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17. Thompson Sampling Beginner's Exercise Prompt-en_US.srt
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17. Thompson Sampling Beginner's Exercise Prompt.mp4
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18. Thompson Sampling Code-en_US.srt
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18. Thompson Sampling Code.mp4
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19. Thompson Sampling With Gaussian Reward Theory-en_US.srt
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19. Thompson Sampling With Gaussian Reward Theory.mp4
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2. Applications of the Explore-Exploit Dilemma-en_US.srt
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2. Applications of the Explore-Exploit Dilemma.mp4
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2. BONUS Where to get discount coupons and FREE deep learning material-en_US.srt
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2. BONUS Where to get discount coupons and FREE deep learning material.mp4
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2. Course Outline and Big Picture-en_US.srt
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2. Course Outline and Big Picture.mp4
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2. From Bandits to Full Reinforcement Learning-en_US.srt
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2. From Bandits to Full Reinforcement Learning.mp4
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2. Gridworld-en_US.srt
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2. Gridworld.mp4
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2. How to Code by Yourself (part 2)-en_US.srt
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2. How to Code by Yourself (part 2).mp4
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2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow-en_US.srt
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2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4
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2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4
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2. Iterative Policy Evaluation-en_US.srt
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2. Iterative Policy Evaluation.mp4
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2. Linear Models for Reinforcement Learning-en_US.srt
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2. Linear Models for Reinforcement Learning.mp4
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2. Monte Carlo Policy Evaluation-en_US.srt
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2. Monte Carlo Policy Evaluation.mp4
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2. Stock Trading Project Section Introduction-en_US.srt
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2. Stock Trading Project Section Introduction.mp4
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2. TD(0) Prediction-en_US.srt
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2. TD(0) Prediction.mp4
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20. Thompson Sampling With Gaussian Reward Code-en_US.srt
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20. Thompson Sampling With Gaussian Reward Code.mp4
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21. Why don't we just use a library-en_US.srt
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21. Why don't we just use a library.mp4
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22. Nonstationary Bandits-en_US.srt
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22. Nonstationary Bandits.mp4
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23. Bandit Summary, Real Data, and Online Learning-en_US.srt
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23. Bandit Summary, Real Data, and Online Learning.mp4
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24. (Optional) Alternative Bandit Designs-en_US.srt
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24. (Optional) Alternative Bandit Designs.mp4
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25. Suggestion Box-en_US.srt
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25. Suggestion Box.mp4
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3. Choosing Rewards-en_US.srt
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3. Choosing Rewards.mp4
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3. Data and Environment-en_US.srt
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3. Data and Environment.mp4
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3. Designing Your RL Program-en_US.srt
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3. Designing Your RL Program.mp4
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3. Epsilon-Greedy Theory-en_US.srt
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3. Epsilon-Greedy Theory.mp4
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3. External URLs.txt
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3. Feature Engineering-en_US.srt
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3. Feature Engineering.mp4
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3. Machine Learning and AI Prerequisite Roadmap (pt 1)-en_US.srt
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3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4
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3. Monte Carlo Policy Evaluation in Code-en_US.srt
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3. Monte Carlo Policy Evaluation in Code.mp4
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3. Proof that using Jupyter Notebook is the same as not using it-en_US.srt
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3. Proof that using Jupyter Notebook is the same as not using it.mp4
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3. TD(0) Prediction in Code-en_US.srt
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3. TD(0) Prediction in Code.mp4
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3. Where to get the Code-en_US.srt
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3. Where to get the Code.mp4
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4. Approximation Methods for Prediction-en_US.srt
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4. Approximation Methods for Prediction.mp4
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4. Calculating a Sample Mean (pt 1)-en_US.srt
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4. Calculating a Sample Mean (pt 1).mp4
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4. Gridworld in Code-en_US.srt
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4. Gridworld in Code.mp4
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4. How to Model Q for Q-Learning-en_US.srt
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4. How to Model Q for Q-Learning.mp4
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4. How to Succeed in this Course-en_US.srt
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4. How to Succeed in this Course.mp4
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4. Machine Learning and AI Prerequisite Roadmap (pt 2)-en_US.srt
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4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4
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4. Monte Carlo Control-en_US.srt
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4. Monte Carlo Control.mp4
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4. Python 2 vs Python 3-en_US.srt
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4. Python 2 vs Python 3.mp4
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4. SARSA-en_US.srt
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4. SARSA.mp4
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4. The Markov Property-en_US.srt
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4. The Markov Property.mp4
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5. Approximation Methods for Prediction Code-en_US.srt
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5. Approximation Methods for Prediction Code.mp4
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5. Design of the Program-en_US.srt
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5. Design of the Program.mp4
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5. Epsilon-Greedy Beginner's Exercise Prompt-en_US.srt
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5. Epsilon-Greedy Beginner's Exercise Prompt.mp4
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5. Iterative Policy Evaluation in Code-en_US.srt
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5. Iterative Policy Evaluation in Code.mp4
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5. Markov Decision Processes (MDPs)-en_US.srt
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5. Markov Decision Processes (MDPs).mp4
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5. Monte Carlo Control in Code-en_US.srt
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5. Monte Carlo Control in Code.mp4
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5. SARSA in Code-en_US.srt
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5. SARSA in Code.mp4
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5. Warmup-en_US.srt
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5. Warmup.mp4
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6. Approximation Methods for Control-en_US.srt
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6. Approximation Methods for Control.mp4
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6. Code pt 1-en_US.srt
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6. Code pt 1.mp4
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6. Designing Your Bandit Program-en_US.srt
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6. Designing Your Bandit Program.mp4
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6. Future Rewards-en_US.srt
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6. Future Rewards.mp4
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6. Monte Carlo Control without Exploring Starts-en_US.srt
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6. Monte Carlo Control without Exploring Starts.mp4
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6. Q Learning-en_US.srt
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6. Q Learning.mp4
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6. Windy Gridworld in Code-en_US.srt
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6. Windy Gridworld in Code.mp4
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7. Approximation Methods for Control Code-en_US.srt
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7. Approximation Methods for Control Code.mp4
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7. Code pt 2-en_US.srt
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7. Code pt 2.mp4
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7. Epsilon-Greedy in Code-en_US.srt
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7. Epsilon-Greedy in Code.mp4
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7. Iterative Policy Evaluation for Windy Gridworld in Code-en_US.srt
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7. Iterative Policy Evaluation for Windy Gridworld in Code.mp4
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7. Monte Carlo Control without Exploring Starts in Code-en_US.srt
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7. Monte Carlo Control without Exploring Starts in Code.mp4
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7. Q Learning in Code-en_US.srt
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7. Q Learning in Code.mp4
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7. Value Functions-en_US.srt
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7. Value Functions.mp4
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8. CartPole-en_US.srt
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8. CartPole.mp4
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8. Code pt 3-en_US.srt
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8. Code pt 3.mp4
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8. Comparing Different Epsilons-en_US.srt
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8. Comparing Different Epsilons.mp4
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8. Monte Carlo Summary-en_US.srt
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8. Monte Carlo Summary.mp4
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8. Policy Improvement-en_US.srt
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8. Policy Improvement.mp4
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8. TD Learning Section Summary-en_US.srt
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8. TD Learning Section Summary.mp4
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8. The Bellman Equation (pt 1)-en_US.srt
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8. The Bellman Equation (pt 1).mp4
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9. CartPole Code-en_US.srt
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9. CartPole Code.mp4
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9. Code pt 4-en_US.srt
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9. Code pt 4.mp4
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9. Optimistic Initial Values Theory-en_US.srt
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9. Optimistic Initial Values Theory.mp4
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9. Policy Iteration-en_US.srt
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9. Policy Iteration.mp4
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9. The Bellman Equation (pt 2)-en_US.srt
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9. The Bellman Equation (pt 2).mp4
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