by Lauren Washington | Released February 2018 | ISBN: 9781787129344

Get to grips with the basics of Reinforcement Learning and build your own intelligent systemsAbout This VideoDeep dive into the concepts and explore practical coding samples in R and PythonThis fast-paced guide will give you a better understanding of everything about RL concepts, frameworks, algorithms, and morePractical, real-world examples will help you get acquainted with the various concepts in RLIn DetailReinforcement 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. Show and hide more

1. Chapter 1 : Setting Up Your Environment
   • The Course Overview 00:03:33
   • Install RStudio 00:02:41
   • Install Python 00:01:47
   • Launch Jupyter Notebook 00:03:39
2. Chapter 2 : Shallow Dive into Reinforcement Learning
   • Learning Type Distinctions 00:02:26
   • Get Started with Reinforcement Learning 00:02:42
   • Real-world Reinforcement Learning Examples 00:02:14
   • Key Terms in Reinforcement Learning 00:04:12
3. Chapter 3 : Monte Carlo Method and OpenAI Gym
   • OpenAI Gym 00:03:54
   • Monte Carlo Method 00:05:56
   • Monte Carlo Method in Python 00:02:18
   • Monte Carlo Method in R 00:03:08
   • Practical Reinforcement Learning in OpenAI Gym 00:01:58
4. Chapter 4 : Markov Decision Process
   • Markov Decision Process Concepts 00:07:44
   • Python MDP Toolbox 00:06:41
   • Value and Policy Iteration in Python 00:03:32
   • MDP Toolbox in R 00:02:49
   • Value Iteration and Policy Iteration in R 00:03:10
5. Chapter 5 : Temporal Difference Learning
   • Temporal Difference Learning 00:08:24
   • Temporal Difference Learning in Python 00:01:53
   • Temporal Difference Learning in R 00:02:54
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