8 papers
A Temporal Barrier Framework for Collision Avoidance in Multi-Agent Autonomous Aerial Vehicles
Benedikt Barthel Sorensen, Mitchell Black, Erfaun Noorani +1
Operating teams of autonomous aircraft in dynamic, uncertain, and potentially adversarial environments requires safety protocols that are reliable yet selective, and allow agents t…
Ergodicity in reinforcement learning
Dominik Baumann, Erfaun Noorani, Arsenii Mustafin +5
In reinforcement learning, we typically aim to optimize the expected value of the sum of rewards an agent collects over a trajectory. However, if the process generating these rewar…
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis
Rui Liu, Anish Gupta, Erfaun Noorani +1
Reinforcement Learning (RL) has shown exceptional performance across various applications, enabling autonomous agents to learn optimal policies through interaction with their envir…
Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems
Armin Lederer, Erfaun Noorani, Andreas Krause
Ensuring safety in multi-agent systems is a significant challenge, particularly in settings where centralized coordination is impractical. In this work, we propose a novel risk-sen…
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy
Erfaun Noorani, Zachary Serlin, Ben Price +1
The DARPA Transfer from Imprecise and Abstract Models to Autonomous Technologies (TIAMAT) program aims to address rapid and robust transfer of autonomy technologies across dynamic…
Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures
Erfaun Noorani, Pasan Dissanayake, Faisal Hamman +1
Counterfactual explanations indicate the smallest change in input that can translate to a different outcome for a machine learning model. Counterfactuals have generated immense int…