Reinforcement Learning Explained for Beginners

The course focuses on the practical applications of RL and includes a hands-on project.

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Course curriculum

1. Overview on Reinforcement Learning Course
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2. Introduction to Course and Instructor
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3. Introduction to Instructor
1. What is Reinforcement Learning
2. What is Reinforcement Learning Hiders and Seekers by OpenAI
3. RL vs Other ML Frameworks
4. Why Reinforcement Learning
5. Examples of Reinforcement Learning
6. Limitations of Reinforcement Learning
7. Exercises
1. What is Environment
2. What is Environment_2
3. What is Agent
4. What is State
5. State Belongs to Environment and not to Agent
6. What is Action
7. What is Reward
8. Goal
9. Policy
10. Summary
1. Setup 1
2. Setup 2
3. Setup 3
4. Policy Comparison
5. Deterministic Environment
6. Stochastic Environment
7. Stochastic Environment 2
8. Stochastic Environment 3
9. Non Stationary Environment
10. GridWorld Summary
11. Activity
1. Probability
2. Probability 2
3. Probability 3
4. Conditional Probability
5. Conditional Probability Fun Example
6. Joint Probability
7. Joint probability 2
8. Joint Probability 3
9. Expected Value
10. Conditional Expectation
11. Modeling Uncertainity of Environment
12. Modeling Uncertainity of Environment 2
13. Modeling Uncertainity of Environment 3
14. Modeling Uncertainity of Environment Stochastic Policy
15. Modeling Uncertainity of Environment Stochastic Policy 2
16. Modeling Uncertainity of Environment Value Functions
17. Running Averages
18. Running Averages as Temporal Difference
19. Activity
1. Markov Property
2. State Space
3. Action Space
4. Transition Probabilities
5. Reward Function
6. Discount Factor
7. Summary
8. Activity
1. MOR Quiz 1
2. MOR Quiz Solution 1
3. MOR Quiz 2
4. MOR Quiz Solution 2
5. MOR Reward Scaling
6. MOR Infinite Horizons
7. MOR Quiz 3
8. MOR Quiz Solution 3
1. MDP Recap
2. Value Functions
3. Optimal Value Function
4. Optimal Policy
5. Balman Equation
6. Value Iteration
7. Value Iteration Quiz
8. Value Iteration Quiz Gamma Missing
9. Value Iteration Solution
10. Problems of Value Iteration
11. Policy Evaluation
12. Policy Evaluation 2
13. Policy Evaluation 3
14. Policy Evaluation Closed Form Solution
15. Policy Iteration
16. State Action Values
17. V and Q Comparisons
1. What does it mean that MDP is Unknown
2. Why Transition Probabilities are Important
3. Model Based Solutions
4. Model Free Solutions
5. Monte-Carlo Learning
6. Monte-Carlo Learning Example
7. Monte-Carlo Learning Limitations
1. Running Average
2. Learning Rate
3. Learning Equation
4. TD Algorithm
5. Exploration vs Exploitation
6. Epsilon Greedy Policy
7. SARSA
8. Q-Learning
9. Q-Learning Implementation for MAPROVER Clipped
1. N Step Look a Head
2. Formulation
3. Values
4. TD Eligibility Trace
5. TD Q-Learning TD Lambda
1. Frozenlake 1
2. Frozenlake Implementation

About this course

$199.99
106 lessons
9 hours of video content

Reinforcement Learning Explained for Beginners

Course curriculum

Introduction to Course and Instructor

Motivation Reinforcement Learning

Terminology of Reinforcement Learning

GridWorld Example

Markov Decision Process Prerequisites

Elements of Markov Decision Process

More on Reword

Solving MDP

Value Approximation

Temporal Differencing-Q Learning

TD Lambda

Project Frozenlake (Open AI Gym)

About this course