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.
Ce cours fait partie de la Spécialisation Apprentissage par renforcement
Offert par
À propos de ce cours
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode
Compétences que vous acquerrez
- 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
Programme de cours : ce que vous apprendrez dans ce cours
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
Avis
- 5 stars81,88 %
- 4 stars13,67 %
- 3 stars2,87 %
- 2 stars0,60 %
- 1 star0,95 %
Meilleurs avis pour SAMPLE-BASED LEARNING METHODS
Excellent paced course that helped me understand sample based methods. Assignments were thoroughly build to practically utilize these concepts
Everything is great overall but It would be more better if DynaQ & DynaQ+ were explained more detail in the lecture instead of assignment.
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
definitely interesting subjects, but I do not like the teaching method. Very mechanic and dull, with not enough connection to the real world
À propos du Spécialisation Apprentissage par renforcement

Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Une aide financière est-elle possible ?
D'autres questions ? Visitez le Centre d'Aide pour les Étudiants.