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Introduction to Reinforcement Learning
Understand basic concept and terminology of Reinforcement Learning
Reinforcement learning (RL) is learning by interacting with an environment. A Reinforcement Learning agent learns from the consequences of its actions, rather than from being explicitly taught and it selects its actions on basis of its past experiences (exploitation) and also by new choices (exploration), which are essentially, trial and error learning just like a child learns. The reinforcement signal that the RL-agent receives is a numerical reward, which encodes the success of an action’s outcome, and the agent seeks to learn to select actions that maximize the accumulated reward over time.
For learning anything new, understanding basic terminology is very important. To understand RL, the reader has to get familiar with below terminology, their meaning and above all how these terms are linked to each other, but before that let’s see one example to understand what Reinforcement Learning means.
