Jonathan P. Shock

According to our database, Jonathan P. Shock authored at least 16 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Opportunities of Reinforcement Learning in South Africa's Just Transition.

Claude Formanek, Callum Rhys Tilbury, Jonathan P. Shock

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

Coordination Failure in Cooperative Offline MARL.

Callum Rhys Tilbury, Claude Formanek, Louise Beyers, Jonathan P. Shock, Arnu Pretorius

CoRR, 2024

Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation.

Claude Formanek, Callum Rhys Tilbury, Louise Beyers, Jonathan P. Shock, Arnu Pretorius

CoRR, 2024

Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation.

Juan Formanek, Callum Rhys Tilbury, Louise Beyers, Jonathan P. Shock, Arnu Pretorius

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

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

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

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

Policy-based Reinforcement Learning for Generalisation in Interactive Text-based Environments.

Edan Toledo, Jan Buys, Jonathan P. Shock

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023

Off-the-Grid MARL: Datasets and Baselines for Offline Multi-Agent Reinforcement Learning.

Claude Formanek, Asad Jeewa, Jonathan P. Shock, Arnu Pretorius

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022

Brain Structural Saliency over the Ages.

Daniel Taylor, Jonathan P. Shock, Deshendran Moodley, Jonathan Ipser, Matthias Treder

Proceedings of the Machine Learning, Optimization, and Data Science, 2022

2021

Causal Multi-Agent Reinforcement Learning: Review and Open Problems.

St John Grimbly, Jonathan P. Shock, Arnu Pretorius

CoRR, 2021

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, Alexandre Laterre, Karim Beguir

CoRR, 2021

2020

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness.

Emily Muller, Jonathan P. Shock, Andreas Bender, Julian Kleeberger, Tobias Högen, Martin Justinus Rosenfelder, Bubacarr Bah, Alex Lopez-Rolon

Comput. Biol. Medicine, 2019

2015

Probability Density Functions from the Fisher Information Metric.

T. Clingman, Jeff Murugan, Jonathan P. Shock

CoRR, 2015