Reinforcement Learning

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Reinforcement Learning (RL)

Autonomous systems that learn to make excellent decisions are required to realize AI’s dreams and effects. One strong paradigm for doing so is reinforcement learning, which applies to a wide range of applications, including robotics, gaming, consumer modeling, and healthcare. This session will provide a thorough introduction to the area of reinforcement learning, and students will learn about the different types of RL.

Students will be taught about the fundamental issues and approaches, such as generalization and exploration. Students will become well-versed in key principles and approaches for RL through a combination of lectures, writing, and coding exercises. The essential issues and methodologies, such as generalization and exploration, will be presented to students. Through a combination of lectures, writing, and coding assignments, students will gain a thorough understanding of important RL principles and methodologies.

 

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What Will You Learn?

  • Q-Learning
  • Q-Learning in Depth
  • Gradient of Policy
  • Critic of Actors
  • Gradient of Deep Deterministic Policy (DDPG)
  • DDPG with double delays (TD3)
  • Deep Reinforcement Learning's Foundation Techniques
  • How to build a cutting-edge AI model that outperforms even the most difficult virtual applications

Course Content

Reinforcement Learning

  • Lecture 1 – Introduction
    00:00
  • Lecture 2 – Given a Model of the World
    00:00
  • Lecture 3 – Model-Free Policy Evaluation
    00:00
  • Lecture 4 – Model Free Control
    00:00
  • Lecture 5 – Value Function Approximation
    00:00
  • Lecture 6 – CNNs and Deep Q Learning
    00:00
  • Lecture 7 – Imitation Learning
    00:00
  • Lecture 8 – Policy Gradient
    00:00
  • Lecture 9 – Policy Gradient II
    00:00
  • Lecture 10 – Policy Gradient III & Review
    00:00
  • Lecture 11 – Fast Reinforcement Learning
    00:00
  • Lecture 12 – Fast Reinforcement Learning II
    00:00
  • Lecture 13 – Fast Reinforcement Learning III
    00:00
  • Lecture 14 – Batch Reinforcement Learning
    00:00
  • Lecture 15 – Monte Carlo Tree Search
    00:00

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