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%
來自SAMPLE-BASED LEARNING METHODS的熱門評論
Good balance of theory and programming assignments. I really like the weekly bonus videos with professors and developers. Recommend to everyone.
Great course! The notebooks are a perfect level of difficulty for someone learning RL for the first time. Thanks Martha and Adam for all your work on this!! Great content!!
Overall a very nice course, well explained and presented.
Sometimes, it would be nice to see the slides 'full screen' rather than the small version in the corner.
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.