Introduction to Reinforcement Learning in Python

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Coursera Project Network
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在此指導項目中,您將:

Implement Monte Carlo techniques for RL

Implement Temporal Difference algorithms in Python

Implement Q-learning in Python

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

In this project-based course, we will explore Reinforcement Learning in Python. Reinforcement Learning, or RL for short, is different from supervised learning methods in that, rather than being given correct examples by humans, the AI finds the correct answers for itself through a predefined framework of reward signals. In this course, we will discuss theories and concepts that are integral to RL, such as the Multi-Arm Bandit problem and its implications, and how Markov Decision processes can be leveraged to find solutions. Then we will implement code examples in Python of basic Temporal Difference algorithms and Monte Carlo techniques. Finally, we implement an example of Q-learning in Python. I would encourage learners to experiment with the tools and methods discussed in this course. The learner is highly encouraged to experiment beyond the scope of the course. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

您要培養的技能

Artificial General IntelligenceMachine LearningAritificial IntelligenceAgent-Based ModelReinforcement Learning in Python

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Learn about the Multi-Arm Bandit problem and the exploration vs. exploitation trade-off

  2. Understand Markov Decision Processes

  3. Implement Monte Carlo techniques for RL

  4.  Implement Temporal Difference algorithms in Python

  5. Implement Q-learning in Python

指導項目工作原理

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