3 papers
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.AI2025
Pointwise-in-Time Explanation for Linear Temporal Logic Rules
Noel Brindise, Cedric Langbort
The new field of Explainable Planning (XAIP) has produced a variety of approaches to explain and describe the behavior of autonomous agents to human observers. Many summarize agent…