Introduction to Reinforcement Learning in Python

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Neste projeto guiado, você irá:

Implement Monte Carlo techniques for RL

Implement Temporal Difference algorithms in Python

Implement Q-learning in Python

Clock2 hours
CloudSem necessidade de download
VideoVídeo em tela dividida
Comment DotsInglês
LaptopApenas em desktop

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.

Habilidades que você desenvolverá

Artificial General IntelligenceMachine LearningAritificial IntelligenceAgent-Based ModelReinforcement Learning in Python

Aprender passo a passo

Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:

  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

Como funcionam os projetos guiados

Sua área de trabalho é um espaço em nuvem, acessado diretamente do navegador, sem necessidade de nenhum download

Em um vídeo de tela dividida, seu instrutor te orientará passo a passo

Perguntas Frequentes – FAQ

Perguntas Frequentes – FAQ

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