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Reinforcement Learning with Python Explained for Beginners
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[TutsNode.com] - Reinforcement Learning with Python Explained for Beginners (Size: 3.35 GB) (Files: 349)
[TutsNode.com] - Reinforcement Learning with Python Explained for Beginners
10. Temporal Differencing-Q Learning
10. Q-Learning Implementation for MAPROVER Clipped.mp4
10. Q-Learning Implementation for MAPROVER Clipped.srt
2. Learning Rate.srt
9. Q-Learning.srt
10.1 QLearning_MAPROVER.ipynb
1. Running Average.srt
3. Activity TD Learning Rate Python.srt
5. TD Algorithm.srt
4. Learning Equation.srt
7. Epsilon Greedy Policy.srt
8. SARSA.srt
6. Exploration vs Exploitation.srt
11. SARSA MAPRover Activity.srt
9. Q-Learning.mp4
2. Learning Rate.mp4
1. Running Average.mp4
5. TD Algorithm.mp4
8. SARSA.mp4
4. Learning Equation.mp4
3. Activity TD Learning Rate Python.mp4
7. Epsilon Greedy Policy.mp4
6. Exploration vs Exploitation.mp4
11. SARSA MAPRover Activity.mp4
1. Introduction to Course and Instructor
1.1 Reinforcement Learning Introduction.pptx
2. Link to oneDrive and Github to get the Python Notebooks.html
1. Introduction to Course and Instructor.srt
1. Introduction to Course and Instructor.mp4
12. Project Frozenlake (Open AI Gym)
2. Frozenlake Implementation.srt
3. Implementation Frozen Lake Numpy Activity.srt
2. Frozenlake Implementation.mp4
2.1 FrozenLake-gym.ipynb
4. THANK YOU.srt
1. Frozenlake 1.srt
4. THANK YOU.mp4
1. Frozenlake 1.mp4
3. Implementation Frozen Lake Numpy Activity.mp4
1.1 Reinforcement Learning Complete.pptx
4. GridWorld Example
8. Stochastic Environment 3.srt
9. Non Stationary Environment.srt
4. Policy Comparison.srt
6. Stochastic Environment.srt
5. Deterministic Environment.srt
3. Setup 3.srt
10. GridWorld Summary.srt
7. Stochastic Environment 2.srt
2. Setup 2.srt
11. Activity.srt
1. Setup 1.srt
9. Non Stationary Environment.mp4
3. Setup 3.mp4
8. Stochastic Environment 3.mp4
6. Stochastic Environment.mp4
4. Policy Comparison.mp4
5. Deterministic Environment.mp4
2. Setup 2.mp4
10. GridWorld Summary.mp4
7. Stochastic Environment 2.mp4
1. Setup 1.mp4
11. Activity.mp4
8. Solving MDP
9. Value Iteration Solution.srt
17. Policy Iteration.srt
12. Policy Evaluation.srt
19. State Action Values.srt
11. Problems of Value Iteration.srt
5. Bellman Equation.srt
4. Optimal Policy.srt
20. V and Q Comparisons.srt
2. Value Functions.srt
10. Value Iteration Solution Activity Value Iteration Python.srt
14. Policy Evaluation 3.srt
13. Policy Evaluation 2.srt
3. Optimal Value Function.srt
15. Policy Evaluation Closed Form Solution.srt
6. Value Iteration.srt
16. Policy Evaluation ClosedFormSolution Activity Policy Evaluation Python.srt
7. Value Iteration Quiz.srt
18. Policy Iteration Activity Policy Iteration Python.srt
1. MDP Recap.srt
8. Value Iteration Quiz Gamma Missing.srt
9. Value Iteration Solution.mp4
12. Policy Evaluation.mp4
17. Policy Iteration.mp4
19. State Action Values.mp4
10. Value Iteration Solution Activity Value Iteration Python.mp4
5. Bellman Equation.mp4
11. Problems of Value Iteration.mp4
14. Policy Evaluation 3.mp4
3. Optimal Value Function.mp4
4. Optimal Policy.mp4
13. Policy Evaluation 2.mp4
2. Value Functions.mp4
6. Value Iteration.mp4
7. Value Iteration Quiz.mp4
20. V and Q Comparisons.mp4
16. Policy Evaluation ClosedFormSolution Activity Policy Evaluation Python.mp4
15. Policy Evaluation Closed Form Solution.mp4
18. Policy Iteration Activity Policy Iteration Python.mp4
1. MDP Recap.mp4
8. Value Iteration Quiz Gamma Missing.mp4
3. Terminology of Reinforcement Learning
7. What is Reward.srt
10. Summary.srt
4. What is State.srt
2. What is Environment_2.srt
6. What is Action.srt
3. What is Agent.srt
5. State Belongs to Environment and not to Agent.srt
9. Policy.srt
8. Goal.srt
1. What is Environment.srt
7. What is Reward.mp4
10. Summary.mp4
5. State Belongs to Environment and not to Agent.mp4
2. What is Environment_2.mp4
3. What is Agent.mp4
4. What is State.mp4
6. What is Action.mp4
9. Policy.mp4
8. Goal.mp4
1. What is Environment.mp4
2. Motivation Reinforcement Learning
1. What is Reinforcement Learning.srt
3. RL vs Other ML Frameworks.srt
6. Limitations of Reinforcement Learning.srt
7. Request for Your Honest Review.srt
8. Exercises.srt
2. What is Reinforcement Learning Hiders and Seekers by OpenAI.srt
5. Examples of Reinforcement Learning.srt
4. Why Reinforcement Learning.srt
2. What is Reinforcement Learning Hiders and Seekers by OpenAI.mp4
3. RL vs Other ML Frameworks.mp4
6. Limitations of Reinforcement Learning.mp4
1. What is Reinforcement Learning.mp4
7. Request for Your Honest Review.mp4
4. Why Reinforcement Learning.mp4
5. Examples of Reinforcement Learning.mp4
8. Exercises.mp4
9. Value Approximation
6. Monte-Carlo Learning Example.srt
3. Model Based Solutions.srt
5. Monte-Carlo Learning.srt
2. Why Transition Probabilities are Important.srt
7. Monte-Carlo Learning Limitations.srt
1. What does it mean that MDP is Unknown.srt
4. Model Free Solutions.srt
6. Monte-Carlo Learning Example.mp4
5. Monte-Carlo Learning.mp4
2. Why Transition Probabilities are Important.mp4
3. Model Based Solutions.mp4
7. Monte-Carlo Learning Limitations.mp4
4. Model Free Solutions.mp4
1. What does it mean that MDP is Unknown.mp4
5. Markov Decision Process Prerequisites
16. Modeling Uncertainity of Environment Value Functions.srt
9. Expected Value.srt
5. Conditional Probability Fun Example.srt
4. Conditional Probability.srt
2. Probability 2.srt
18. Running Averages 2.srt
11. Modeling Uncertainity of Environment.srt
19. Running Averages as Temporal Difference.srt
12. Modeling Uncertainity of Environment 2.srt
3. Probability 3.srt
7. Joint probability 2.srt
1. Probability.srt
13. Modeling Uncertainity of Environment 3.srt
6. Joint Probability.srt
15. Modeling Uncertainity of Environment Stochastic Policy 2.srt
14. Modeling Uncertainity of Environment Stochastic Policy.srt
17. Running Averages.srt
20. Activity.srt
8. Joint Probability 3.srt
10. Conditional Expectation.srt
16. Modeling Uncertainity of Environment Value Functions.mp4
4. Conditional Probability.mp4
2. Probability 2.mp4
5. Conditional Probability Fun Example.mp4
9. Expected Value.mp4
3. Probability 3.mp4
11. Modeling Uncertainity of Environment.mp4
1. Probability.mp4
12. Modeling Uncertainity of Environment 2.mp4
8. Joint Probability 3.mp4
14. Modeling Uncertainity of Environment Stochastic Policy.mp4
13. Modeling Uncertainity of Environment 3.mp4
19. Running Averages as Temporal Difference.mp4
18. Running Averages 2.mp4
7. Joint probability 2.mp4
15. Modeling Uncertainity of Environment Stochastic Policy 2.mp4
6. Joint Probability.mp4
10. Conditional Expectation.mp4
20. Activity.mp4
17. Running Averages.mp4
7. More on Reward
2. MOR Quiz Solution 1.srt
6. MOR Infinite Horizons.srt
5. MOR Reward Scaling.srt
4. MOR Quiz Solution 2.srt
8. MOR Quiz Solution 3.srt
1. MOR Quiz 1.srt
3. MOR Quiz 2.srt
7. MOR Quiz 3.srt
2. MOR Quiz Solution 1.mp4
8. MOR Quiz Solution 3.mp4
5. MOR Reward Scaling.mp4
4. MOR Quiz Solution 2.mp4
6. MOR Infinite Horizons.mp4
1. MOR Quiz 1.mp4
3. MOR Quiz 2.mp4
7. MOR Quiz 3.mp4
11. TD Lambda
5. TD Q-Learning TD Lambda.srt
2. Formulation.srt
4. TD Eligibility Trace.srt
1. N Step Look a Head.srt
3. Values.srt
6. TD Q Learning TD Lambda TD(Lambda) MAPRover Activity.srt
5. TD Q-Learning TD Lambda.mp4
2. Formulation.mp4
1. N Step Look a Head.mp4
4. TD Eligibility Trace.mp4
3. Values.mp4
6. TD Q Learning TD Lambda TD(Lambda) MAPRover Activity.mp4
6. Elements of Markov Decision Process
7. Summary.srt
5. Reward Function.srt
1. Markov Property.srt
2. State Space.srt
6. Discount Factor.srt
4. Transition Probabilities.srt
3. Action Space.srt
8. Activity.srt
5. Reward Function.mp4
2. State Space.mp4
3. Action Space.mp4
4. Transition Probabilities.mp4
7. Summary.mp4
6. Discount Factor.mp4
1. Markov Property.mp4
8. Activity.mp4
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