Recent Releases of sigmarl
sigmarl - 1.4.0
Evaluation for the paper "A Real-Time Control Barrier Function-Based Safety Filter for Motion Planning with Arbitrary Road Boundary Constraints."
Abstract—We present a real-time safety filter for motion planning, such as learning-based methods, using Control Barrier Functions (CBFs), which provides formal guarantees for collision avoidance with road boundaries. A key feature of our approach is its ability to directly incorporate road geometries of arbitrary shape without resorting to conservative overapproximations. We formulate the safety filter as a constrained optimization problem in the form of a Quadratic Program (QP). It achieves safety by making minimal, necessary adjustments to the control actions issued by the nominal motion planner. We validate our safety filter through extensive numerical experiments across a variety of traffic scenarios featuring complex roads. The results confirm its reliable safety and high computational efficiency (execution frequency up to 40 Hz)..
Preprint: https://arxiv.org/abs/2505.02395
- Python
Published by Jianye-Xu 11 months ago
sigmarl - 1.3.0
Evaluation for the paper "High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation."
Abstract— We examine the complexity of the standard High-Order Control Barrier Function (HOCBF) approach and propose a truncated Taylor-based approach that reduces design parameters. First, we derive the explicit inequality condition for the HOCBF approach and show that the corresponding equality condition sets a lower bound on the barrier function value that regulates its decay rate. Next, we present our Truncated Taylor CBF (TTCBF), which uses a truncated Taylor series to approximate the discrete-time CBF condition. While the standard HOCBF approach requires multiple class K functions, leading to more design parameters as the constraint's relative degree increases, our TTCBF approach requires only one. We support our theoretical findings in numerical collision-avoidance experiments and show that our approach ensures safety while reducing design complexity.
Preprint: https://doi.org/10.48550/arXiv.2503.15014
- Python
Published by Jianye-Xu about 1 year ago
sigmarl - 1.2.0
Evaluation for the paper "XP-MARL: Auxiliary Prioritization in Multi-Agent Reinforcement Learning to Address Non-Stationarity."
Abstract—Non-stationarity poses a fundamental challenge in Multi-Agent Reinforcement Learning (MARL), arising from agents simultaneously learning and altering their policies. This creates a non-stationary environment from the perspective of each individual agent, often leading to suboptimal or even unconverged learning outcomes. We propose an open-source framework named XP-MARL, which augments MARL with auxiliary prioritization to address this challenge in cooperative settings. XP-MARL is 1) founded upon our hypothesis that prioritizing agents and letting higher-priority agents establish their actions first would stabilize the learning process and thus mitigate non-stationarity and 2) enabled by our proposed mechanism called action propagation, where higher-priority agents act first and communicate their actions, providing a more stationary environment for others. Moreover, instead of using a predefined or heuristic priority assignment, XP-MARL learns priority-assignment policies with an auxiliary MARL problem, leading to a joint learning scheme. Experiments in a motion-planning scenario involving Connected and Automated Vehicles (CAVs) demonstrate that XP-MARL improves the safety of a baseline model by 84.4% and outperforms a state-of-the-art approach, which improves the baseline by only 12.8%.
Preprint: https://doi.org/10.48550/arXiv.2409.11852
- Python
Published by Jianye-Xu over 1 year ago
sigmarl - 1.1.0
Evaluation for the paper "SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning". This paper was accepted by the 27th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2024).
- Python
Published by Jianye-Xu over 1 year ago