Parker-Holder, Jack

Effective Diversity in Population-Based Reinforcement Learning

Parker-Holder, J., Pacchiano, A., Choromanski, K., & Roberts, S. (2020) · arXiv

Reinforcement Learning in RoboticsEvolutionary Algorithms and Applications

Cited by 60

Evolving Curricula with Regret-Based Environment Design

Parker-Holder, J., Jiang, M., Dennis, M., Samvelyan, M., Foerster, J., Grefenstette, E., & Rocktäschel, T. (2022) · arXiv

Reinforcement Learning in RoboticsEvolutionary Algorithms and Applications

Cited by 28

Genie: Generative Interactive Environments

Bruce, J., Dennis, M., Edwards, A., Parker-Holder, J., Shi, Y., Hughes, E., Lai, M., Mavalankar, A., Steigerwald, R., Apps, C., et al. (2024) · arXiv

Reinforcement Learning in RoboticsGenerative Adversarial Networks and Image Synthesis

Cited by 23

Replay-Guided Adversarial Environment Design

Jiang, M., Dennis, M., Parker-Holder, J., Foerster, J., Grefenstette, E., & Rocktäschel, T. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 21

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

Samvelyan, M., Kirk, R., Kurin, V., Parker-Holder, J., Jiang, M., Hambro, E., Petroni, F., Küttler, H., Grefenstette, E., & Rocktäschel, T. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 20

Human-Timescale Adaptation in an Open-Ended Task Space

Adaptive Agent Team, Bauer, J., Baumli, K., Baveja, S., Behbahani, F., Bhoopchand, A., Bradley-Schmieg, N., Chang, M., Clay, N., Collister, A., et al. (2023) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 19

Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment

Ball, P. J., Lu, C., Parker-Holder, J., & Roberts, S. (2021) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 11

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning

Samvelyan, M., Khan, A., Dennis, M., Jiang, M., Parker-Holder, J., Foerster, J., Raileanu, R., & Rocktäschel, T. (2023) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 8

Open-Endedness is Essential for Artificial Superhuman Intelligence

Hughes, E., Dennis, M., Parker-Holder, J., Behbahani, F., Mavalankar, A., Shi, Y., Schaul, T., & Rocktaschel, T. (2024) · arXiv

Psychiatry, Mental Health, NeuroscienceArtificial Intelligence Applications

Cited by 4

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games

Paglieri, D., Cupiał, B., Coward, S., Piterbarg, U., Wolczyk, M., Khan, A., Pignatelli, E., Kuciński, Ł., Pinto, L., Fergus, R., et al. (2024) · arXiv

Artificial Intelligence in GamesMultimodal Machine Learning Applications

Cited by 2

Insights From the NeurIPS 2021 NetHack Challenge

Hambro, E., Mohanty, S., Babaev, D., Byeon, M., Chakraborty, D., Grefenstette, E., Jiang, M., Jo, D., Kanervisto, A., Kim, J., et al. (2022) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1

Stabilizing Unsupervised Environment Design with a Learned Adversary

Mediratta, I., Jiang, M., Parker-Holder, J., Dennis, M., Vinitsky, E., & Rocktäschel, T. (2023) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 1