Bi-Level Reinforcement Learning for Sim-to-Real Adaptation
A recent study published on arXiv (identifier 2510.17709) introduces a bi-level reinforcement learning method aimed at tackling the sim-to-real gap, a prevalent issue when training policies in simulated environments for real-world applications. This method is pertinent to both sim-to-real RL and dyna-style model-based RL, particularly where real-world interactions incur high costs. The research points out that policies developed in simulations frequently fail to perform well in actual settings due to differences between the simulation and reality, referred to as the sim-to-real gap. This gap arises from a mismatch in objectives: simulation models focus on predictive accuracy, while policies seek to optimize task performance. The authors propose that by analyzing how sensitive the learned policy is to simulation parameters, one can adapt the simulation model through gradient-based methods to enhance real-world outcomes. This paper has been classified as a replace-cross type and can be accessed via the provided URL.
Key facts
- Paper arXiv:2510.17709 proposes a bi-level reinforcement learning pathway for sim-to-real adaptation.
- The approach addresses the sim-to-real gap in reinforcement learning.
- It is applicable to sim-to-real RL and dyna-style model-based RL.
- The sim-to-real gap is caused by discrepancies between simulation and real-world environments.
- The gap reflects an objective mismatch: simulation models prioritize predictive accuracy, while policies aim to maximize task performance.
- The method uses gradient-based adaptation of the simulation model based on policy sensitivity to simulation parameters.
- The paper is announced as a replace-cross type on arXiv.
- The full text is available at https://arxiv.org/abs/2510.17709.
Entities
Institutions
- arXiv