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cs.LG2026
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…
cs.LG2025
"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.…
cs.LG2023
Online Decision Making with History-Average Dependent Costs (Extended)
Vijeth Hebbar, Cedric Langbort
In many online sequential decision-making scenarios, a learner's choices affect not just their current costs but also the future ones. In this work, we look at one particular case…