Symmetry aware reinforcement learning cuts training data needs
Sample Complexity of Equivariant Reinforcement Learning
Computational ComplexityMachine Learning
Summary
Teaching robots to perform tasks takes a lot of practice, which can be expensive and slow. This paper shows that when robots know about the symmetries in their environment—like how rotating or flipping something doesn't change its properties—they can learn much faster. By using ideas from math about these symmetries, the authors found ways to reduce how many tries a robot needs to get good at a task. They also tested their ideas on robot simulations and saw real improvements in learning speed.
What this means in practice
- •For robotics engineers: Design robots that learn control strategies faster by incorporating symmetry knowledge to reduce training time and data needs.
- •For simulation software developers: Implement symmetry-aware learning modules in simulation platforms to speed up development and testing of robot behaviors.
Authors
Rayan Mazouz, Haibo Zhao, Chris Hillar, Christian Shewmake
Abstract
Reinforcement learning (RL) is a powerful framework for robotic control, yet its practical application is often hindered by high sample complexity. This is particularly restrictive in physical domains where interaction data is costly. While the world often exhibits geometric and physical symmetries, standard RL algorithms typically fail to exploit this structure. In this paper, we demonstrate that exploiting group symmetries significantly reduces the sample complexity of RL. Focusing on finite-horizon Markov decision processes, we find that leveraging homomorphisms induced by group symmetries significantly reduces the theoretical upper and lower bounds on the number of environment interactions required to reach an optimal return. We further extend these bounds to continuous state and action spaces, providing corresponding sample-complexity guarantees under appropriate regularity assumptions. Beyond theory, we validate our findings through controlled experiments and demonstrate the advantages of symmetry-aware policy learning on high-dimensional continuous robotic simulations. Our results show that integrating symmetry into the learning pipeline yields substantial gains in sample efficiency and performance, offering a principled path toward more data-efficient robotics.