Sasha Abramowitz

According to our database, Sasha Abramowitz authored at least 14 papers between 2020 and 2026.

Collaborative distances:

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

2026

Self-Supervised On-Policy Reinforcement Learning via Contrastive Proximal Policy Optimisation.

Asim Osman, Sasha Abramowitz, Mark Bergh, Ulrich A. Mbou Sob, Ruan John de Kock, Omayma Mahjoub, Oussama Hidaoui, Noah de Nicola, Arnol Fokam, Félix Chalumeau, Daniel Rajaonarivonivelomanantsoa, Siddarth Singh, Refiloe Shabe, Juan Claude Formanek, Simon du Toit, Arnu Pretorius

CoRR, May, 2026

2025

Oryx: a Performant and Scalable Algorithm for Many-Agent Coordination in Offline MARL.

Claude Formanek, Omayma Mahjoub, Louay Ben Nessir, Sasha Abramowitz, Ruan de Kock, Wiem Khlifi, Simon du Toit, Félix Chalumeau, Daniel Rajaonarivonivelomanantsoa, Arnol Fokam, Siddarth Singh, Ulrich A. Mbou Sob, Arnu Pretorius

CoRR, May, 2025

Breaking the Performance Ceiling in Complex Reinforcement Learning requires Inference Strategies.

Félix Chalumeau, Daniel Rajaonarivonivelomanantsoa, Ruan de Kock, Claude Formanek, Sasha Abramowitz, Omayma Mahjoub, Wiem Khlifi, Simon du Toit, Louay Ben Nessir, Refiloe Shabe, Arnol Fokam, Siddarth Singh, Ulrich A. Mbou Sob, Arnu Pretorius

CoRR, May, 2025

Oryx: a Scalable Sequence Model for Many-Agent Coordination in Offline MARL.

Juan Claude Formanek, Omayma Mahjoub, Louay Ben Nessir, Sasha Abramowitz, Ruan John de Kock, Wiem Khlifi, Daniel Rajaonarivonivelomanantsoa, Simon du Toit, Arnol Fokam, Siddarth Singh, Ulrich A. Mbou Sob, Félix Chalumeau, Arnu Pretorius

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Multi-Agent Reinforcement Learning with Selective State-Space Models.

Jemma Daniel, Ruan John de Kock, Louay Ben Nessir, Sasha Abramowitz, Omayma Mahjoub, Wiem Khlifi, Juan Claude Formanek, Arnu Pretorius

Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, 2025

Sable: a Performant, Efficient and Scalable Sequence Model for MARL.

Omayma Mahjoub, Sasha Abramowitz, Ruan John de Kock, Wiem Khlifi, Simon du Toit, Jemma Daniel, Louay Ben Nessir, Louise Beyers, Juan Claude Formanek, Liam Clark, Arnu Pretorius

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Performant, Memory Efficient and Scalable Multi-Agent Reinforcement Learning.

Omayma Mahjoub, Sasha Abramowitz, Ruan de Kock, Wiem Khlifi, Simon du Toit, Jemma Daniel, Louay Ben Nessir, Louise Beyers, Claude Formanek, Liam Clark, Arnu Pretorius

CoRR, 2024

Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX.

Clément Bonnet, Daniel Luo, Donal Byrne, Shikha Surana, Sasha Abramowitz, Paul Duckworth, Vincent Coyette, Laurence Illing Midgley, Elshadai Tegegn, Tristan Kalloniatis, Omayma Mahjoub, Matthew Macfarlane, Andries P. Smit, Nathan Grinsztajn, Raphaël Boige, Cemlyn N. Waters, Mohamed A. Mimouni, Ulrich A. Mbou Sob, Ruan de Kock, Siddarth Singh, Daniel Furelos-Blanco, Victor Le, Arnu Pretorius, Alexandre Laterre

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Generalisable Agents for Neural Network Optimisation.

Kale-ab Abebe Tessera, Callum Rhys Tilbury, Sasha Abramowitz, Ruan de Kock, Omayma Mahjoub, Benjamin Rosman, Sara Hooker, Arnu Pretorius

CoRR, 2023

Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX.

Clément Bonnet, Daniel Luo, Donal Byrne, Shikha Surana, Vincent Coyette, Paul Duckworth, Laurence I. Midgley, Tristan Kalloniatis, Sasha Abramowitz, Cemlyn N. Waters, Andries P. Smit, Nathan Grinsztajn, Ulrich A. Mbou Sob, Omayma Mahjoub, Elshadai Tegegn, Mohamed A. Mimouni, Raphaël Boige, Ruan de Kock, Daniel Furelos-Blanco, Victor Le, Arnu Pretorius, Alexandre Laterre

CoRR, 2023

2022

Scalable evolutionary hierarchical reinforcement learning.

Sasha Abramowitz, Geoff Nitschke

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

Towards Run-time Efficient Hierarchical Reinforcement Learning.

Sasha Abramowitz, Geoff Nitschke

Proceedings of the IEEE Congress on Evolutionary Computation, 2022

2021

Mava: a research library for distributed multi-agent reinforcement learning in JAX.

Ruan de Kock, Omayma Mahjoub, Sasha Abramowitz, Wiem Khlifi, Callum Rhys Tilbury, Claude Formanek, Andries P. Smit, Arnu Pretorius

CoRR, 2021

2020

Efficiently Coevolving Deep Neural Networks and Data Augmentations.

Shane Acton, Sasha Abramowitz, Liron Toledo, Geoff Nitschke

Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020