Learning Reward Machines: A Study in Partially Observable Reinforcement Learning
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Date
2023
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Abstract
Reinforcement Learning (RL) is a machine learning paradigm wherein an artificial agentinteracts with an environment with the purpose of learning behaviour that maximizesthe expected cumulative reward it receives from the environment. Reward machines(RMs) provide a structured, automata-based representation of a reward function thatenables an RL agent to decompose an RL problem into structured subproblems that canbe efficiently learned via off-policy learning. Here we show that RMs can be learnedfrom experience, instead of being specified by the user, and that the resulting problemdecomposition can be used to effectively solve partially observable RL problems. We posethe task of learning RMs as a discrete optimization problem where the objective is to findan RM that decomposes the problem into a set of subproblems such that the combinationof their optimal memoryless policies is an optimal policy for the original problem. Weshow the effectiveness of this approach on three partially observable domains, where itsignificantly outperforms A3C, PPO, and ACER, and discuss its advantages, limitations,and broader potential.
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Keywords
Reinforcement learning, Reward machines, Partial observability, Automata learning