This article explains its definition, how it functions, and its primary applications.
In reinforcement learning, AI is rewarded for desired actions and punished for undesired actions.
Reinforcement learning can only take place in a controlled environment.
Andrii Shyp/Getty Images
At the same, it will learn to avoid punitive actions that cause it to lose points.
For example, reinforcement learning has trained AI to play video games.
The AI learns how to achieve the game’s goals through trial and error.
Reinforcement learningalgorithmshave even been used to train robots to walk and perform other physical tasks.
Reinforcement learning has also shown promise in statistics, simulation, engineering, manufacturing, and medical research.
For example, a robot could use reinforcement learning to navigate a room where everything is stationary.
The robot would just aimlessly bump into things without developing a clear picture of its surroundings.
Since this learning relies on trial and error, it can consume more time and resources.
On the plus side, reinforcement learning doesn’t require much human supervision.
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-free algorithms are useful for more dynamic, real-world situations.
Applications of DQN range from predicting the stock market to regulating air quality in large buildings.
FAQ
Q-learning is another term for model-free algorithms.
A “policy” is a plan that a reinforcement learning system uses to solve problems.
