Search Results for author: Rick Valenzano

Found 1 papers, 1 papers with code

Learning Reward Machines for Partially Observable Reinforcement Learning

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

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