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Found 2 papers, 1 papers with code
Learning Reward Machines: A Study in Partially Observable Reinforcement Learning
Here we show that RMs can be learned from experience, instead of being specified by the user, and that the resulting problem decomposition can be used to effectively solve partially observable RL problems.
Partially Observable Reinforcement Learning
Problem Decomposition
+2
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
