1 code implementation • NeurIPS 2019 • Rodrigo Toro Icarte, Ethan Waldie, Toryn Klassen, Rick Valenzano, Margarita Castro, Sheila Mcilraith
Reward Machines (RMs), originally proposed for specifying problems in Reinforcement Learning (RL), provide a structured, automata-based representation of a reward function that allows an agent to decompose problems into subproblems that can be efficiently learned using off-policy learning.
Partially Observable Reinforcement Learning Problem Decomposition +2
1 code implementation • ICML 2018 • Rodrigo Toro Icarte, Toryn Klassen, Richard Valenzano, Sheila Mcilraith
In this paper we propose Reward Machines {—} a type of finite state machine that supports the specification of reward functions while exposing reward function structure to the learner and supporting decomposition.