Reinforcement learning is a machine learning and, by definition, artificial intelligence (AI) technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. This came to us from Analytics Insight in their article, “Reinforcement Learning For a Better Tomorrow.”
Reinforcement learning makes use of algorithms that do not rely only on historical data sets to learn to make a prediction or perform a task.
As compared to unsupervised learning, reinforcement learning is different in terms of goals. While the goal in unsupervised learning is to find similarities and differences between data points, in reinforcement learning the goal is to find a suitable action model that would maximize the total cumulative reward of the agent.
This gives you a good idea of how the technology works. Most organizations have little knowledge of how AI systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning are being applied.
Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. “Explainable AI” is used to describe an AI model, its expected impact and potential biases.
Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.
