Reinforcement Learning (RL) is a machine learning paradigm inspired by how humans learn. In RL, algorithms called “agents” interact with a simulated environment. In turn, the simulated environment provides feedback to the agent based on what actions it takes. The RL agent continues taking actions and the environment continues providing feedback until the RL agent optimizes a task. Hence, RL has achieved high performances in applications where one’s actions have consequences, and those consequences may be delayed in time. Examples of applications where RL has been successfully deployed include self-driving vehicles, automated stock trading, customized healthcare, robot manipulation, and natural language processing.

Current research on this topic at FINS concerns Inverse Reinforcement Learning (IRL), which is the opposite of RL. While RL involves designing agents that optimize some task, IRL involves designing algorithms that understand said agents. IRL helps humans infer how RL agents operate, what they are doing, and what they will do next. IRL has many applications within the domain of human-computer interaction, such as human interpretability of agents and efficient agent training from human experts.