Chapter 3
Robot Control System Using Deep Reinforcement Learning
Section 4
Reinforcement Learning Basics
Reinforcement learning aims to create algorithms that can learn and adapt to environmental changes. This programming technique is based on the concept of receiving external stimuli that depend on the actions chosen by the agent. A correct choice will involve a reward, while an incorrect choice will lead to a penalty. The goal of the system is to achieve the best possible result, of course. Here are the topics that we will cover now: - Reinforcement Learning Basics - Agent's Interaction with the Environment - Agent-Environment Interface - Agent-Environment - Reinforcement Learning Terminology - Reinforcement Learning Algorithms - Decision Process (DP) - Policy - Monte Carlo (MC) Methods - Temporal Difference (TD) Learning