5 papers
Steady-state Based Approach to Online Non-stochastic Control
Vijeth Hebbar, Spencer Hutchinson, Mahnoosh Alizadeh +1
We study the problem of online non-stochastic control (ONC), which is the control of a linear system under adversarial disturbances and adversarial cost functions, with the aim of…
Live LTL Progress Tracking: Towards Task-Based Exploration
Noel Brindise, Cedric Langbort, Melkior Ornik
Motivated by the challenge presented by non-Markovian objectives in reinforcement learning (RL), we present a novel framework to track and represent the progress of autonomous agen…
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)
Noel Brindise, Vijeth Hebbar, Riya Shah +1
In this work, we provide an extended discussion of a new approach to explainable Reinforcement Learning called Diverse Near-Optimal Alternatives (DNA), first proposed at L4DC 2025.…
Revisiting Regret Benchmarks in Online Non-Stochastic Control
Vijeth Hebbar, Cédric Langbort
In the online non-stochastic control problem, an agent sequentially selects control inputs for a linear dynamical system when facing unknown and adversarially selected convex costs…
Responding to Promises: No-regret learning against followers with memory
Vijeth Hebbar, Cédric Langbort
We consider a repeated Stackelberg game setup where the leader faces a sequence of followers of unknown types and must learn what commitments to make. While previous works have con…