Zero-Shot Policy Transfer in Reinforcement Learning using Buckingham's Pi Theorem

• URL: http://arxiv.org/abs/2510.08768v1
• Date: Thu, 09 Oct 2025 19:36:18 GMT
• Title: Zero-Shot Policy Transfer in Reinforcement Learning using Buckingham's Pi Theorem
• Authors: Francisco Pascoa, Ian Lalonde, Alexandre Girard,
• Abstract summary: Reinforcement learning policies often fail to generalize to new robots, tasks, or environments with different physical parameters.<n>This paper presents a simple, zero-shot transfer method based on Buckingham's Pi Theorem to address this limitation.
• Score: 42.37643072381109
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL) policies often fail to generalize to new robots, tasks, or environments with different physical parameters, a challenge that limits their real-world applicability. This paper presents a simple, zero-shot transfer method based on Buckingham's Pi Theorem to address this limitation. The method adapts a pre-trained policy to new system contexts by scaling its inputs (observations) and outputs (actions) through a dimensionless space, requiring no retraining. The approach is evaluated against a naive transfer baseline across three environments of increasing complexity: a simulated pendulum, a physical pendulum for sim-to-real validation, and the high-dimensional HalfCheetah. Results demonstrate that the scaled transfer exhibits no loss of performance on dynamically similar contexts. Furthermore, on non-similar contexts, the scaled policy consistently outperforms the naive transfer, significantly expanding the volume of contexts where the original policy remains effective. These findings demonstrate that dimensional analysis provides a powerful and practical tool to enhance the robustness and generalization of RL policies.

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