Practical Intro To Reinforcement Learning Using Robotics
https://DevCourseWeb.com
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.47 GB | Duration: 2h 40m
Learn to build robots that can automatically learn to behave well in their environments without explicit instructions.
What you'll learn
Understanding the differences between Reinforcement Learning and the other AI paradigms.
Identifying areas in robotics where Reinforcement Learning can be applied.
Framing problems in robotics as Reinforcement Learning problems.
Applying the Multi-Armed Bandit algorithm to help robots to automatically learn expected behaviours.
Developing reward functions for Reinforcement Learning in robotic contexts.
Using Python to control sensor and actuators on EV3 Mindstorms robots.
Programming simple robots that can automatically learn the best actions to take in their environments.
Understanding the multi-armed bandit algorithm.
Balancing exploration and exploitation using various strategies.
Requirements
Be able to understand basic OOP programs in Python.
Basic algebra skills. If you know how to add, subtract, multiply, and divide number, then you are fit for this course.
Description
Diving into Reinforcement Learning can seem daunting if you don't have the proper hands-on guidance. Many times, people have asked me whether they should master Deep Learning before delving into Reinforcement Learning and my answer has always been that "it depends on what you want to do with RL". RL is a broad domain in its own respect. There are classical RL algorithms that can be learned and applied without any Deep Learning experience. There is also Deep Reinforcement Learning which leverages neural networks to help RL agents to learn proper behaviors in their environment through trials-and-error with reward functions.
