Improving Controller Generalization with Dimensionless Markov Decision Processes

• URL: http://arxiv.org/abs/2504.10006v1
• Date: Mon, 14 Apr 2025 09:08:53 GMT
• Title: Improving Controller Generalization with Dimensionless Markov Decision Processes
• Authors: Valentin Charvet, Sebastian Stein, Roderick Murray-Smith,
• Abstract summary: We propose a Model-Based approach to increase generalization where both world model and policy are trained in a dimensionless state-action space.<n>We demonstrate the applicability of our method on simulated actuated pendulum and cartpole systems, where policies trained on a single environment are robust to shifts in the distribution of the context.
• Score: 6.047438841182958
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Controllers trained with Reinforcement Learning tend to be very specialized and thus generalize poorly when their testing environment differs from their training one. We propose a Model-Based approach to increase generalization where both world model and policy are trained in a dimensionless state-action space. To do so, we introduce the Dimensionless Markov Decision Process ($\Pi$-MDP): an extension of Contextual-MDPs in which state and action spaces are non-dimensionalized with the Buckingham-$\Pi$ theorem. This procedure induces policies that are equivariant with respect to changes in the context of the underlying dynamics. We provide a generic framework for this approach and apply it to a model-based policy search algorithm using Gaussian Process models. We demonstrate the applicability of our method on simulated actuated pendulum and cartpole systems, where policies trained on a single environment are robust to shifts in the distribution of the context.

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