Learning through experience
Amii researchers are pioneers and leaders in the field of reinforcement learning (RL), a branch of machine learning that enables AI systems to learn through experience. RL systems interact with their environments, often through trial and error, earning positive or negative rewards based on their actions. Humans define the overall task and relevant rewards that the system uses to discover the best action to take in a given situation.
Instead of being told what actions to take to achieve a goal, the system must learn which actions yield the most reward by trying them. Over time, the system develops a policy (or way of acting) that lets it select the action that will best achieve the goal, which can help us discover the optimal actions to take in a given scenario.
Reinforcement learning can be used for process optimization and improvement, as part of a recommender or intelligent tutoring system, and for adaptive control and decision making in autonomous systems.