Practical Reinforcement Learning - Agents and Environments

Get to grips with the basics of Reinforcement Learning and build your own intelligent systems

Video Description
Reinforcement Learning (RL) has become one of the hottest research areas in ML and AI, and is expected to have widespread usage in diverse areas such as neuroscience, psychology, and more.

You can make an intelligent agent in a few steps: have it semi-randomly explore different choices of movement to actions given different conditions and states, then keep track of the reward or penalty associated with each choice for a given state or action.

In this course, you’ll learn how to code the core algorithms in RL and get to know the algorithms in both R and Python. This video course will help you hit the ground running, with R and Python code for Value Iteration, Policy Gradients, Q-Learning, Temporal Difference Learning, the Markov Decision Process, and Bellman Equations, which provides a framework for modeling decision making where outcomes are partly random and partly under the control of a decision maker.

At the end of the video course, you’ll know the main concepts and key algorithms in RL.

Style and Approach
This comprehensive course is a step-by-step guide that will help you understand reinforcement learning. Practical, real-world examples will help you get acquainted with the various concepts in reinforcement learning. This course provides practical reinforcement examples in R and Python.

What You Will Learn
Work with Discount Factor Methods
Utilize the Markov Decision Process and Bellman equations
Get to know the key terms in RL
Dive into Temporal Difference Learning, an algorithm that combines Monte Carlo methods and dynamic programming
Take your machine learning skills to the next level with RL techniques