3 papers
cs.LG2024
Bayesian Inverse Reinforcement Learning for Non-Markovian Rewards
Noah Topper, Alvaro Velasquez, George Atia
Inverse reinforcement learning (IRL) is the problem of inferring a reward function from expert behavior. There are several approaches to IRL, but most are designed to learn a Marko…
cs.LG2021
Inferring Probabilistic Reward Machines from Non-Markovian Reward Processes for Reinforcement Learning
Taylor Dohmen, Noah Topper, George Atia +3
The success of reinforcement learning in typical settings is predicated on Markovian assumptions on the reward signal by which an agent learns optimal policies. In recent years, th…
econ.TH2020
Functional Decision Theory in an Evolutionary Environment
Noah Topper
Functional decision theory (FDT) is a fairly new mode of decision theory and a normative viewpoint on how an agent should maximize expected utility. The current standard in decisio…