Probability Density Functions from the Fisher Information Metric. - T. Clingman; Jeff Murugan; Jonathan P. Shock (CoRR, 2015)

Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness. - Emily Muller; Jonathan P. Shock; Andreas Bender 003; Julian Kleeberger; Tobias Högen; Martin Justinus Rosenfelder; Bubacarr Bah; Alex Lopez-Rolon (Comput. Biol. Medicine, 2019)

A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning. - Arnu Pretorius; Scott Alexander Cameron; Elan Van Biljon; Tom Makkink; Shahil Mawjee; Jeremy du Plessis; Jonathan P. Shock; Alexandre Laterre; Karim Beguir (NeurIPS, 2020)

Mava: a research framework for distributed multi-agent reinforcement learning. - Arnu Pretorius; Kale-ab Abebe Tessera; Andries P. Smit; Claude Formanek; St John Grimbly; Kevin Eloff; Siphelele Danisa; Lawrence Francis; Jonathan P. Shock; Herman Kamper; Willie Brink; Herman A. Engelbrecht; et al. (CoRR, 2021)

Causal Multi-Agent Reinforcement Learning: Review and Open Problems. - St John Grimbly; Jonathan P. Shock; Arnu Pretorius (CoRR, 2021)

Brain Structural Saliency over the Ages. - Daniel Taylor; Jonathan P. Shock; Deshendran Moodley; Jonathan Ipser; Matthias Treder (LOD (2), 2022)

Off-the-Grid MARL: Datasets and Baselines for Offline Multi-Agent Reinforcement Learning. - Claude Formanek; Asad Jeewa; Jonathan P. Shock; Arnu Pretorius (AAMAS, 2023)

Policy-based Reinforcement Learning for Generalisation in Interactive Text-based Environments. - Edan Toledo; Jan Buys; Jonathan P. Shock (EACL, 2023)

Off-the-Grid MARL: a Framework for Dataset Generation with Baselines for Cooperative Offline Multi-Agent Reinforcement Learning. - Claude Formanek; Asad Jeewa; Jonathan P. Shock; Arnu Pretorius (CoRR, 2023)

Reduce, Reuse, Recycle: Selective Reincarnation in Multi-Agent Reinforcement Learning. - Claude Formanek; Callum Rhys Tilbury; Jonathan P. Shock; Kale-ab Abebe Tessera; Arnu Pretorius (CoRR, 2023)

Planning to Learn: A Novel Algorithm for Active Learning during Model-Based Planning. - Rowan Hodson; Bruce Bassett; Charel van Hoof; Benjamin Rosman; Mark Solms; Jonathan P. Shock; Ryan Smith (CoRR, 2023)

Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation. - Juan Claude Formanek; Callum Rhys Tilbury; Louise Beyers; Jonathan P. Shock; Arnu Pretorius (NeurIPS, 2024)

Coordination Failure in Cooperative Offline MARL. - Callum Rhys Tilbury; Claude Formanek; Louise Beyers; Jonathan P. Shock; Arnu Pretorius (CoRR, 2024)

Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning. - Claude Formanek; Louise Beyers; Callum Rhys Tilbury; Jonathan P. Shock; Arnu Pretorius (CoRR, 2024)

Opportunities of Reinforcement Learning in South Africa's Just Transition. - Claude Formanek; Callum Rhys Tilbury; Jonathan P. Shock (CoRR, 2024)

Is an Exponentially Growing Action Space Really that Bad? Validating a Core Assumption for using Multi-Agent RL. - Ruan de Kock; Arnu Pretorius; Jonathan P. Shock (AAMAS, 2025)