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Found 2 papers, 2 papers with code
Learning Reward Machines for Partially Observable Reinforcement Learning
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
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
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.
