# RANDPOL: Parameter-Efficient End-to-End Quadruped Locomotion via Randomized Policy Learning

arXiv: https://arxiv.org/abs/2505.19054 · HTML: https://mlsanity.com/paper/2505.19054/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 25 May 2025.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots](https://mlsanity.com/paper/2306.14874/) (2023)
- [Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning](https://mlsanity.com/paper/2105.08328/) (2021)
- [CPG-RL: Learning Central Pattern Generators for Quadruped Locomotion](https://mlsanity.com/paper/2211.00458/) (2022)
- [Deep Randomized Neural Networks](https://mlsanity.com/paper/2002.12287/) (2020)
- [Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learning](https://mlsanity.com/paper/2103.06484/) (2021)

## Abstract (arXiv)

Modern learning-based locomotion controllers typically rely on fully trainable deep neural networks with a large number of parameters. This paper studies a different design point for end-to-end control: whether effective quadruped locomotion can be achieved with a drastically reduced trainable parameter space. We present RANDomized POlicy Learning (RANDPOL), a policy learning approach in which the hidden layers of the actor and critic are randomly initialized and fixed, while only the final linear readout is trained. This yields a parameter-efficient controller class that retains nonlinear expressiveness through a fixed random basis while substantially reducing the dimension of the optimization problem. RANDPOL is supported by the mathematical foundation of randomized function approximation, which provides a principled basis for using fixed random nonlinear features as expressive function classes. We evaluate RANDPOL on end-to-end locomotion control for the Unitree Go2 quadruped and compare it with Proximal Policy Optimization (PPO). The results show that RANDPOL attains comparative locomotion performance with far fewer trainable parameters, lower learning-phase computation time per iteration, and a favorable performance-complexity trade-off. We further demonstrate successful zero-shot sim-to-real transfer of the learned RANDPOL controller on the physical Unitree Go2 under user-issued forward-velocity and yaw-rate commands. These results indicate that, for structured robotic control problems, reducing trainable complexity can remain compatible with effective simulated and real-world performance.