Machine Learning: An Introduction to Reinforcement Learning
Sheikh Amir Fayaz, Sumina Sidiq, Majid Zaman, Muheet Ahmed Butt · 2022
Reinforcement Learning (RL) is a prevalent prototype for finite sequential decision making under improbability. A distinctive RL algorithm functions with only restricted knowledge of the environment and with limited response or feedback on the quality of the conclusions. To work efficiently in complex environments, learning agents entail the capability to form convenient generalizations, that is, the ability to selectively overlook extraneous facts. It is a challenging task to develop a single illustration that is suitable for a large problem setting. This chapter provides a brief introduction to reinforcement learning models, procedures, techniques, and reinforcement learning processes. Particular focus is on the aspects related to the agent-environment interface and how Reinforcement Learning can be used in various daily life practical applications. The basic concept that we will explore is that of a solution to the Re-enforcement Learning problem using the Markov Decision Process (MDP). We assume that the reader has a basic idea of machine learning concepts and algorithms.