In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning.
This course is part of the Reinforcement Learning Specialization
Offered By
About this Course
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode
Skills you will gain
- Artificial Intelligence (AI)
- Machine Learning
- Reinforcement Learning
- Function Approximation
- Intelligent Systems
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode
Syllabus - What you will learn from this course
Welcome to the Course!
Monte Carlo Methods for Prediction & Control
Temporal Difference Learning Methods for Prediction
Temporal Difference Learning Methods for Control
Planning, Learning & Acting
Reviews
- 5 stars81.99%
- 4 stars13.59%
- 3 stars2.85%
- 2 stars0.60%
- 1 star0.95%
TOP REVIEWS FROM SAMPLE-BASED LEARNING METHODS
One of most accurate, precise and well explained courses I have ever had with Coursera. Congratulations for teachers and course creators.
Pretty clear explanations! Nice starting point if you want to deep dive into RL. It gives clear picture over some confusing terms in RL.
Good balance of theory and programming assignments. I really like the weekly bonus videos with professors and developers. Recommend to everyone.
Great course, giving it 5 stars though it deserves both because the assignments have some serious issues that shouldn't actually be a matter. All the other parts are amazing though. Good job
About the Reinforcement Learning Specialization

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