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Publications Talks Teaching

Publications

Asterisk (*) indicates equal contribution.

Gymnasium: A Standard Interface for Reinforcement Learning Environments.
M. Towers*, A. Kwiatkowski*, J. K. Terry*, J. U. Balis, G. De Cola, T. Deleu, M. Goulão, A. Kallinteris, M. Krimmel, Markus, A. KG, and others. arXiv 2024.
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning.
F. Felten, U. Ucak, H. Azmani, G. Peng, W. Röpke, H. Baier, P. Mannion, D. M. Roijers, J. K. Terry, E.-G. Talbi, and others. arXiv 2024.
Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks.
M. Chevalier-Boisvert, B. Dai, M. Towers, R. Perez-Vicente, L. Willems, S. Lahlou, S. Pal, P. S. Castro, J. K. Terry. NeurIPS 2024.
Some Supervision Required: Incorporating Oracle Policies in Reinforcement Learning via Epistemic Uncertainty Metrics.
J. J. Tai, J. K. Terry, M. S. Innocente, J. Brusey, N. Horri. arXiv 2022.
Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments.
R. Sullivan, J. K. Terry, B. Black, J. P. Dickerson. ICML 2021.
Revisiting parameter sharing in multi-agent deep reinforcement learning.
J. K. Terry, N. Grammel, A. Hari, L. Santos, B. Black. arXiv 2021.
PettingZoo: Gym for multi-agent reinforcement learning.
J. K. Terry, N. Grammel, A. Hari, L. Santos, B. Black. NeurIPS 2021.
Accepted into AAMAS OptLearnMAS 2021 and NeurIPS Deep RL Workshops
Understanding generalization through visualizations.
W. R. Huang, Z. Emam, M. Goldblum, L. Fowl, J. K. Terry, F. Huang, and T. Goldstein. arXiv 2021.
Accepted into NeurIPS ICBINB 2020 workshop, spotlight talk
Statistically Significant Stopping of Neural Network Training.
J. K. Terry, M. Jayakumar, K. De Alwis. arXiv 2021.
Supersuit: Simple microwrappers for reinforcement learning environments.
J. K. Terry, B. Black, A. Hari. arXiv 2020.
Multiplayer support for the arcade learning environment.
J. K. Terry*, B. Black*, L. Santos. arXiv 2020.
Accepted into AAMAS OptLearnMAS 2021 Workshop

Talks

Modern Open Source Reinforcement Learning Tooling
Artificial Intelligence Finance Institute, February 2023
The Farama Foundation
Meta AI Research, January 2023
Umshini: A New Approach to RL Evaluation
Learning in Foundation Environments (LIFE), August 2022
Umshini: A New Approach to RL Evaluation
HuggingFace, July 2022
Gym and the Future of Reinforcement Learning
Open Data Science Conference (ODSC), April 2022
The Future of Gym and Reinforcement Learning
TalkRL Podcast, Dec 2021
Exploring Reward Surfaces in Deep Reinforcement Learning
K-KDD, Oct 2021
Gym and PettingZoo
ML Collective, August 2021
Monte Carlo Emergence Search and the Flocking Learning Environment
Army Research Laboratory, July 2021
PettingZoo
Toronto Synthetic Intelligence Forum, March 2021
Multi-Agent Reinforcement Learning: Systems for Evaluation and Relations to Complex Systems
UMD RLSS, January 2021

Teaching

Introduction to Artificial Intelligence (Undergraduate) | CMSC421
Spring 2019
Deep Learning (Graduate) | CMSC828L
Fall 2018
* At the time of writing, this course number now corresponds to a different course
Applied Machine Learning (Undergraduate) | PHYS476
Spring 2018
* Invited by department to create and teach course; it has now become permanent and is offered annually
Machine Learning for Physicists
Spring 2017
* Semester long seminar