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.
- 5 stars82.04%
- 4 stars13.64%
- 3 stars2.72%
- 2 stars0.61%
- 1 star0.96%
SAMPLE-BASED LEARNING METHODS からの人気レビュー
Excellent paced course that helped me understand sample based methods. Assignments were thoroughly build to practically utilize these concepts
The course is intermediate in difficulty. But it explains the concept very clearly for me to understand difference between different sample based learning methods.
Great course - well paced, with the right material. And the professors deliver content in a structured way, which makes it easier to understand complex concepts.
Well done. Follows Reinforcement Learning (Sutton/Barto) closely and explains topics well. Graded notebooks are invaluable in understanding the material well.