The most popular and comprehensive Open Source ECM platform
Let us know more
Artificial Intelligence: Using Reinforcement Learning to Enable Multi-Step AI Processing
Reinforcement Learning (RL). It is a new name for an area of Operations Research where algorithms are developed that try to optimize input and control parameters to achieve the best cumulative ‘reward’. It’s often a sort of trial and error approach where different possible paths are tested to see which gives the best result.
RL + Big Data and AI techniques make it possible to run large what-if kind of simulations. The key to differentiating Machine Learning from Reinforcement Learning is that ML focuses on a single task like recognizing an image or identifying a pattern across many data sets. RL usually is applied to processes or workflows that are multi-step and tries to optimize possible parameters across multiple steps to achieve an optimal result.
Gary Saarenvirta, CEO of Daisy Intelligence, said that “AI using reinforcement learning is able to run simulations of the future, gaining intelligence from scenarios that will never need to be executed, and therefore… learn faster than traditional test-and-learn approaches, which happen at the pace of time. With massive GPU computing power, simulations can learn years of ” data every day.
Xin Heng, senior Director at Punchh, wrote for Techipedia that “Reinforcement learning acknowledges complexity and recognizes that people are heterogeneous and accounts for these truths, improving each next action over time as the pieces of your game board change around it.”
