Reinforcement learning is a type of Machine learning It is based on rewards and penalties. This article explains its definition, how it works, and its basic applications.
Definition of reinforcement learning
Use programs artificial intelligence (AI) Continuously learning machines improve speed and efficiency. In reinforcement learning, AI is rewarded for desirable actions and punished for undesirable ones.
This learning can only occur in a controlled environment. The programmer assigns positive and negative values (or “points”) to certain behaviors, and the AI can freely explore the environment to obtain rewards and avoid punishments.
Ideally, AI would delay short-term gains in favor of long-term gains. So, if it's given the choice between earning one point in one minute or earning 10 points in two minutes, it will delay gratification and go for the higher value. At the same time, it will learn to avoid punitive actions that would cause it to lose points.
Examples of reinforcement learning
Real-world applications of AI based on reinforcement learning are somewhat limited, but the method has shown promising results in laboratory experiments.
For example, this learning has trained AI to play video games. AI learns how to achieve game objectives through trial and error. For example, in a game like Super Mario Bros., the AI will determine the best way to reach the end of each level while avoiding enemies and obstacles. Dozens of AI programs have successfully beaten specific games, and MuZero even mastered video games it wasn't originally designed to play.
This learning has been used to train enterprise resource management (ERM) software to allocate business resources for optimal long-term outcomes. Reinforcement learning algorithms have been used to train robots to walk and perform other physical tasks. This learning has also shown promise in statistics, simulation, engineering, manufacturing, and medical research.
Its limits
The main limitation of reinforcement learning algorithms is their reliance on a closed environment. For example, a robot can use it to navigate a room where everything is static. However, this learning won't help it navigate a corridor filled with moving people because the environment is constantly changing. The robot will simply aimlessly bump into objects without developing a clear picture of its surroundings.
Because this learning relies on trial and error, it can be time-consuming and resource-intensive. On the upside, reinforcement learning requires little human supervision.
Due to its limitations, it is often combined with other types of machine learning. Self-driving vehicles, for example, use its algorithms in conjunction with other machine learning techniques, such as supervised learning, to navigate roads without collisions.
Types of its algorithms
Reinforcement learning algorithms can be divided into two main categories: model-based or model-free. A model-based algorithm develops a model of its environment to predict the rewards of potential actions. In model-free reinforcement learning, the AI agent learns directly through trial and error.
Model-based algorithms are ideal for simulations and static environments, such as an assembly line, where the goal is to repeat the same action over and over again. Examples of model-based algorithms include value iteration and policy iteration, where an AI agent follows a strict formula (or “policy”) to determine the best course of action.
Model-free algorithms are useful for more dynamic real-world situations. An example of model-free learning is the Deep Q-Network (DQN) algorithm, which uses a neural network to predict outcomes based on past actions and results. DQN applications range from stock market forecasting to air quality regulation in large buildings.
There is a variation of this learning called inverse reinforcement learning, which is when an AI agent learns by observing the actions of humans.
Frequently Asked Questions:
Q1: What is Q-Learning?
The answer: Q-Learning is another term for model-free algorithms. This particular type of reinforcement learning doesn't require a model of the environment to make predictions about; it aims to "learn" the actions taken by different states.
Q2: What is the policy in reinforcement learning?
The answer: A “policy” is a plan a learning system uses to solve problems. It determines what it does and when, based on the information it has and the solution it is trying to achieve.
