Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control. - Ashley Peake; Joe McCalmon; Benjamin Raiford; Tongtong Liu 001; Sarra M. Alqahtani (ICTAI, 2020)

Wilderness Search and Rescue Missions using Deep Reinforcement Learning. - Ashley Peake; Joe McCalmon; Yixin Zhang; Benjamin Raiford; Sarra M. Alqahtani (SSRR, 2020)

A Symbolic-AI Approach for UAV Exploration Tasks. - Yixin Zhang; Joe McCalmon; Ashley Peake; Sarra M. Alqahtani; Paúl Pauca (ICARA, 2021)

Deep Reinforcement Learning for Adaptive Exploration of Unknown Environments. - Ashley Peake; Joe McCalmon; Yixin Zhang; Daniel Myers; Sarra M. Alqahtani; Paúl Pauca (CoRR, 2021)

CAPS: Comprehensible Abstract Policy Summaries for Explaining Reinforcement Learning Agents. - Joe McCalmon; Thai Le; Sarra M. Alqahtani; Dongwon Lee 001 (AAMAS, 2022)

LSTM-Based Anomalous Behavior Detection in Multi-Agent Reinforcement Learning. - Cameron Lischke; Tongtong Liu 001; Joe McCalmon; Md Asifur Rahman; Talal Halabi; Sarra M. Alqahtani (CSR, 2022)

Weaponizing Actions in Multi-Agent Reinforcement Learning: Theoretical and Empirical Study on Security and Robustness. - Tongtong Liu 001; Joe McCalmon; Md Asifur Rahman; Cameron Lischke; Talal Halabi; Sarra M. Alqahtani (PRIMA, 2022)

Safe Reinforcement Learning via Observation Shielding. - Joe McCalmon; Tongtong Liu 001; Reid Goldsmith; Andrew Cyhaniuk; Talal Halabi; Sarra M. Alqahtani (HICSS, 2023)

A novel policy-graph approach with natural language and counterfactual abstractions for explaining reinforcement learning agents. - Tongtong Liu 001; Joe McCalmon; Thai Le; Md Asifur Rahman; Dongwon Lee 001; Sarra M. Alqahtani (Auton. Agents Multi Agent Syst., 2023)