What is Reinforcement Learning?
Reinforcement learning (RL) is a segment of machine learning of artificial intelligence with the main focus on how intelligent agents act in a specific environment for the purpose of maximizing the notion of cumulative reward. In other words, its the ability to learn the relations and associations between stimuli, actions, and the occurrences of events pleasant or unpleasant. Pleasant events refer to rewards but unpleasant events refer to punishments.
Machine learning has three different basic paradigms such as supervised learning, unsupervised learning, and Reinforcement Learning. Reinforcement learning uses dynamic programming in algorithms. Reinforcement learning can be studied in multiple disciplines including game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, and statistics.
In this multi-media article, you will learn about Reinforcement Learning (RL) and will get the answers to some of the most important questions in the theory, such as:
What is Reinforcement Learning & Why is it called so?
What are the types of Reinforcement Learning?
What is Reinforcement Learning in simple words?
Is Reinforcement Learning difficult?
What are the advantages of Reinforcement Learning?
What do you mean by Reinforcement Learning?
What are the similarities and differences between Reinforcement Learning and supervised learning?