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.
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- 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
definitely interesting subjects, but I do not like the teaching method. Very mechanic and dull, with not enough connection to the real world
It's an important course in understanding the working of reinforcement learning. Although some important and complex topics are not explored in this course which are mentioned in the textbook.
Well done. Follows Reinforcement Learning (Sutton/Barto) closely and explains topics well. Graded notebooks are invaluable in understanding the material well.
Pretty clear explanations! Nice starting point if you want to deep dive into RL. It gives clear picture over some confusing terms in RL.