Structural Similarity for Improved Transfer in Reinforcement Learning

• URL: http://arxiv.org/abs/2207.13813v1
• Date: Wed, 27 Jul 2022 22:21:38 GMT
• Title: Structural Similarity for Improved Transfer in Reinforcement Learning
• Authors: C. Chace Ashcraft, Benjamin Stoler, Chigozie Ewulum, Susama Agarwala
• Abstract summary: We present an algorithm that calculates a state similarity measure for states in two finite MDPs based on previously developed bisimulation metrics. We show that the measure satisfies properties of a distance metric and can be used to improve transfer performance for Q-Learning agents.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer learning is an increasingly common approach for developing performant RL agents. However, it is not well understood how to define the relationship between the source and target tasks, and how this relationship contributes to successful transfer. We present an algorithm called Structural Similarity for Two MDPS, or SS2, that calculates a state similarity measure for states in two finite MDPs based on previously developed bisimulation metrics, and show that the measure satisfies properties of a distance metric. Then, through empirical results with GridWorld navigation tasks, we provide evidence that the distance measure can be used to improve transfer performance for Q-Learning agents over previous implementations.

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