Practical Reinforcement Learning - Agents and Environments

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.

Target Audience 

This course is for data Scientists and AI programmers who are new to reinforcement learning and want to learn the fundamentals of building self-learning intelligent agents in a practical way. A basic understanding of machine learning concepts is required.

Business Outcomes 

• Deep dive into the concepts and explore practical coding samples in R and Python
• This fast-paced guide will give you a better understanding of everything about RL concepts, frameworks, algorithms, and more
• Practical, real-world examples will help you get acquainted with the various concepts in RL