Approximation of Lorenz-Optimal Solutions in Multiobjective Markov Decision Processes. - Patrice Perny; Paul Weng; Judy Goldsmith; Josiah Hanna (AAAI (Late-Breaking Developments), 2013)

UT Austin Villa: RoboCup 2015 3D Simulation League Competition and Technical Challenges Champions. - Patrick MacAlpine; Josiah Hanna; Jason Liang; Peter Stone 001 (RoboCup, 2015)

Delta-Tolling: Adaptive Tolling for Optimizing Traffic Throughput. - Guni Sharon; Josiah Hanna; Tarun Rambha; Michael Albert 002; Peter Stone 001; Stephen D. Boyles (ATT@IJCAI, 2016)

High Confidence Off-Policy Evaluation with Models. - Josiah P. Hanna; Peter Stone 001; Scott Niekum (CoRR, 2016)

UT Austin Villa: Project-Driven Research in AI and Robotics. - Katie Genter; Patrick MacAlpine; Jacob Menashe; Josiah Hanna; Elad Liebman; Sanmit Narvekar; Ruohan Zhang; Peter Stone 001 (IEEE Intell. Syst., 2016)

Grounded Action Transformation for Robot Learning in Simulation. - Josiah P. Hanna; Peter Stone 001 (AAAI, 2017)

Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation. - Josiah P. Hanna; Peter Stone 001; Scott Niekum (AAAI, 2017)

Bridging the Gap Between Simulation and Reality. - Josiah P. Hanna (AAMAS, 2017)

Data-Efficient Policy Evaluation Through Behavior Policy Search. - Josiah P. Hanna; Philip S. Thomas; Peter Stone 001; Scott Niekum (ICML, 2017)

Fast and Precise Black and White Ball Detection for RoboCup Soccer. - Jacob Menashe; Josh Kelle; Katie Genter; Josiah Hanna; Elad Liebman; Sanmit Narvekar; Ruohan Zhang; Peter Stone 001 (RoboCup, 2017)

DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation. - Haipeng Chen 001; Bo An 001; Guni Sharon; Josiah P. Hanna; Peter Stone 001; Chunyan Miao; Yeng Chai Soh (AAAI, 2018)

Towards a Data Efficient Off-Policy Policy Gradient. - Josiah P. Hanna; Peter Stone 001 (AAAI Spring Symposia, 2018)

Selecting Compliant Agents for Opt-in Micro-Tolling. - Josiah P. Hanna; Guni Sharon; Stephen D. Boyles; Peter Stone 001 (AAAI, 2019)

Reducing Sampling Error in Policy Gradient Learning. - Josiah P. Hanna; Peter Stone 001 (AAMAS, 2019)

Importance Sampling Policy Evaluation with an Estimated Behavior Policy. - Josiah Hanna; Scott Niekum; Peter Stone 001 (ICML, 2019)

Learning an Interpretable Traffic Signal Control Policy. - James Ault; Josiah P. Hanna; Guni Sharon (AAMAS, 2020)

Reducing Sampling Error in Batch Temporal Difference Learning. - Brahma S. Pavse; Ishan Durugkar; Josiah Hanna; Peter Stone 001 (ICML, 2020)

Stochastic Grounded Action Transformation for Robot Learning in Simulation. - Siddharth Desai; Haresh Karnan; Josiah P. Hanna; Garrett Warnell; Peter Stone 001 (IROS, 2020)

Reinforced Grounded Action Transformation for Sim-to-Real Transfer. - Haresh Karnan; Siddharth Desai; Josiah P. Hanna; Garrett Warnell; Peter Stone 001 (IROS, 2020)

An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch. - Siddharth Desai; Ishan Durugkar; Haresh Karnan; Garrett Warnell; Josiah Hanna; Peter Stone 001 (NeurIPS, 2020)

Quantum-Secure Authentication via Abstract Multi-Agent Interaction. - Ibrahim Ahmed 006; Josiah P. Hanna; Stefano V. Albrecht (CoRR, 2020)

An Imitation from Observation Approach to Sim-to-Real Transfer. - Siddharth Desai; Ishan Durugkar; Haresh Karnan; Garrett Warnell; Josiah Hanna; Peter Stone 001 (CoRR, 2020)

RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration. - Brahma S. Pavse; Faraz Torabi; Josiah Hanna; Garrett Warnell; Peter Stone 001 (IEEE Robotics Autom. Lett., 2020)

A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline Regret. - Sheelabhadra Dey; Sumedh Pendurkar; Guni Sharon; Josiah P. Hanna (IROS, 2021)

Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles. - Josiah P. Hanna; Arrasy Rahman; Elliot Fosong; Francisco Eiras; Mihai Dobre; John Redford; Subramanian Ramamoorthy; Stefano V. Albrecht (IROS, 2021)

Towards Quantum-Secure Authentication and Key Agreement via Abstract Multi-Agent Interaction. - Ibrahim Ahmed 006; Josiah P. Hanna; Elliot Fosong; Stefano V. Albrecht (PAAMS, 2021)

Decoupling Exploration and Exploitation in Reinforcement Learning. - Lukas Schäfer 001; Filippos Christianos; Josiah Hanna; Stefano V. Albrecht (CoRR, 2021)

Robust On-Policy Data Collection for Data-Efficient Policy Evaluation. - Rujie Zhong; Josiah P. Hanna; Lukas Schäfer 001; Stefano V. Albrecht (CoRR, 2021)

Grounded action transformation for sim-to-real reinforcement learning. - Josiah P. Hanna; Siddharth Desai; Haresh Karnan; Garrett Warnell; Peter Stone 001 (Mach. Learn., 2021)

Importance sampling in reinforcement learning with an estimated behavior policy. - Josiah P. Hanna; Scott Niekum; Peter Stone 001 (Mach. Learn., 2021)

Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration. - Lukas Schäfer 001; Filippos Christianos; Josiah P. Hanna; Stefano V. Albrecht (AAMAS, 2022)

Simulation-Acquired Latent Action Spaces for Dynamics Generalization. - Nicholas Corrado; Yuxiao Qu; Josiah P. Hanna (CoLLAs, 2022)

Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning. - Rujie Zhong; Duohan Zhang; Lukas Schäfer 001; Stefano V. Albrecht; Josiah Hanna (NeurIPS, 2022)

ReVar: Strengthening policy evaluation via reduced variance sampling. - Subhojyoti Mukherjee; Josiah P. Hanna; Robert D. Nowak (UAI, 2022)

Multi-agent Databases via Independent Learning. - Chi Zhang 068; Olga Papaemmanouil; Josiah Hanna (CoRR, 2022)

Safe Evaluation For Offline Learning: Are We Ready To Deploy? - Hager Radi; Josiah P. Hanna; Peter Stone 001; Matthew E. Taylor (CoRR, 2022)

Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction. - Brahma S. Pavse; Josiah P. Hanna (AAAI, 2023)

Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning. - Mhairi Dunion; Trevor McInroe; Kevin Sebastian Luck; Josiah P. Hanna; Stefano V. Albrecht (ICLR, 2023)

Conditional Mutual Information for Disentangled Representations in Reinforcement Learning. - Mhairi Dunion; Trevor McInroe; Kevin Sebastian Luck; Josiah Hanna; Stefano V. Albrecht (NeurIPS, 2023)

Multi-task Representation Learning for Pure Exploration in Bilinear Bandits. - Subhojyoti Mukherjee; Qiaomin Xie; Josiah Hanna; Robert D. Nowak (NeurIPS, 2023)

State-Action Similarity-Based Representations for Off-Policy Evaluation. - Brahma S. Pavse; Josiah Hanna (NeurIPS, 2023)

Tackling Unbounded State Spaces in Continuing Task Reinforcement Learning. - Brahma S. Pavse; Yudong Chen 001; Qiaomin Xie; Josiah P. Hanna (CoRR, 2023)

On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling. - Nicholas E. Corrado; Josiah P. Hanna (CoRR, 2023)

Scaling Offline Evaluation of Reinforcement Learning Agents through Abstraction. - Josiah P. Hanna (AAAI, 2024)

SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits. - Subhojyoti Mukherjee; Qiaomin Xie; Josiah P. Hanna; Robert D. Nowak (AISTATS, 2024)

Reinforcement Learning via Auxiliary Task Distillation. - Abhinav Narayan Harish; Larry Heck; Josiah P. Hanna; Zsolt Kira; Andrew Szot (ECCV (81), 2024)

Understanding when Dynamics-Invariant Data Augmentations Benefit Model-free Reinforcement Learning Updates. - Nicholas Corrado; Josiah P. Hanna (ICLR, 2024)

SaVeR: Optimal Data Collection Strategy for Safe Policy Evaluation in Tabular MDP. - Subhojyoti Mukherjee; Josiah P. Hanna; Robert D. Nowak (ICML, 2024)

Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces. - Brahma S. Pavse; Matthew Zurek; Yudong Chen 001; Qiaomin Xie; Josiah P. Hanna (ICML, 2024)

Adaptive Exploration for Data-Efficient General Value Function Evaluations. - Arushi Jain; Josiah Hanna; Doina Precup (NeurIPS, 2024)

Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning. - Nicholas E. Corrado; Yuxiao Qu; John U. Balis; Adam Labiosa; Josiah P. Hanna (RLJ, 2024)

Toward the confident deployment of real-world reinforcement learning agents. - Josiah P. Hanna (AI Mag., 2024)

Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments. - Jeongyeol Kwon; Liu Yang 001; Robert D. Nowak; Josiah Hanna (CoRR, 2024)

Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learning. - Subhojyoti Mukherjee; Josiah P. Hanna; Qiaomin Xie; Robert D. Nowak (CoRR, 2024)

Stable Offline Value Function Learning with Bisimulation-based Representations. - Brahma S. Pavse; Yudong Chen 001; Qiaomin Xie; Josiah P. Hanna (CoRR, 2024)

Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer. - Adam Labiosa; Zhihan Wang; Siddhant Agarwal; William Cong; Geethika Hemkumar; Abhinav Narayan Harish; Benjamin Hong; Josh Kelle; Chen Li; Yuhao Li; Zisen Shao; Peter Stone 001; et al. (CoRR, 2024)

Conservative Evaluation of Offline Policy Learning. - Hager Radi Abdelwahed; Josiah P. Hanna; Matthew E. Taylor (Trans. Mach. Learn. Res., 2024)

Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning. - Kyle Domico; Jean-Charles Noirot Ferrand; Ryan Sheatsley; Eric Pauley; Josiah Hanna; Patrick D. McDaniel (CoRR, 2025)

Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation. - Adam Labiosa; Josiah P. Hanna (CoRR, 2025)

Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation. - Hongyi Zhou; Josiah P. Hanna; Jin Zhu; Ying Yang; Chengchun Shi (CoRR, 2025)

When Can Model-Free Reinforcement Learning be Enough for Thinking? - Josiah P. Hanna; Nicholas E. Corrado (CoRR, 2025)

Centralized Adaptive Sampling for Reliable Co-Training of Independent Multi-Agent Policies. - Nicholas E. Corrado; Josiah P. Hanna (CoRR, 2025)