Reward Translation via Reward Machine in Semi-Alignable MDPs

Yun Hua, Wenhao Li, Bo Jin, Baoxiang Wang, Xiaofeng He, Hongyuan Zha, Xiangfeng Wang

Primary Area: reinforcement learning

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Keywords: Reinforcement Learning

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Abstract:

Deep reinforcement learning often relies heavily on the quality of dense rewards, which can necessitate significant engineering effort. Reusing human-designed rewards across similar tasks in different domains can enhance learning efficiency in reinforcement learning.
Current works have delved into an assortment of domains characterized by divergent embodiments, differing viewpoints, and dynamic disparities. However, these studies require either alignment or alignable demonstrations in which states maintain a bijective map, consequently restricting the applicability to more generalized reward reusing across disparate domains.
It becomes crucial to identify the latent structural similarities through coarser-grained alignments between distinct domains, as this enables a reinforcement learning agent to harness its capacity for abstract transfer in a manner akin to human navigation based on maps.
To address this challenge, semi-alignable Markov Decision Processes (MDPs) is introduced as a fundamental underpinning to delineate the coarse-grained latent structural resemblances amidst varying domains
Subsequently, the Neural Reward Translation (NRT) framework is established, which employs reward machines to resolve cross-domain reward transfer problem within semi-alignable MDPs, thus facilitating more versatile reward reusing that supports reinforcement learning across diverse domains.
Our methodology is corroborated through several semi-alignable environments, highlighting NRT's efficacy in domain adaptation undertakings involving semi-alignable MDPs.

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Submission Number: 7525