Reinforcement Learning with R: Algorithms-Agents-Environment

Learn how to utilize algorithms for reward-based learning, as part of Reinforcement Learning with R.

Created by: Packt Publishing
Tech Knowledge in Motion

What Will I Learn?

• Understand and Implement the "Grid World" Problem in R
• Utilize the Markov Decision Process and Bellman equations
• Get to know the key terms in Reinforcement Learning
• 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
• Learn R examples of policy evaluation and iteration
• Implement typical applications for model-based and model-free RL
• Understand policy evaluation and iteration
• Master Q-Learning with Greedy Selection Examples in R
• Master the Simulated Annealing Changed Discount Factor through examples in R

Requirements

• A basic understanding of Machine Learning concepts is required.

Target audience

• 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.