1 code implementation • 21 Sep 2022 • Rihab Gorsane, Omayma Mahjoub, Ruan de Kock, Roland Dubb, Siddarth Singh, Arnu Pretorius
Combining these recommendations, with novel insights from our analysis, we propose a standardised performance evaluation protocol for cooperative MARL.
no code implementations • 28 Mar 2023 • Siddarth Singh, Benjamin Rosman
In the field of cooperative multi-agent reinforcement learning (MARL), the standard paradigm is the use of centralised training and decentralised execution where a central critic conditions the policies of the cooperative agents based on a central state.
1 code implementation • 16 Jun 2023 • Clément Bonnet, Daniel Luo, Donal Byrne, Shikha Surana, Sasha Abramowitz, Paul Duckworth, Vincent Coyette, Laurence I. Midgley, Elshadai Tegegn, Tristan Kalloniatis, Omayma Mahjoub, Matthew Macfarlane, Andries P. Smit, Nathan Grinsztajn, Raphael 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
Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms.
no code implementations • 13 Dec 2023 • Wiem Khlifi, Siddarth Singh, Omayma Mahjoub, Ruan de Kock, Abidine Vall, Rihab Gorsane, Arnu Pretorius
Cooperative multi-agent reinforcement learning (MARL) has made substantial strides in addressing the distributed decision-making challenges.
no code implementations • 13 Dec 2023 • Omayma Mahjoub, Ruan de Kock, Siddarth Singh, Wiem Khlifi, Abidine Vall, Kale-ab Tessera, Arnu Pretorius
Measuring the contribution of individual agents is challenging in cooperative multi-agent reinforcement learning (MARL).
no code implementations • 13 Dec 2023 • Siddarth Singh, Omayma Mahjoub, Ruan de Kock, Wiem Khlifi, Abidine Vall, Kale-ab Tessera, Arnu Pretorius
Establishing sound experimental standards and rigour is important in any growing field of research.
no code implementations • 12 Feb 2024 • Matthew V Macfarlane, Edan Toledo, Donal Byrne, Siddarth Singh, Paul Duckworth, Alexandre Laterre
SMX demonstrates a statistically significant improvement in performance compared to AlphaZero, as well as demonstrating its performance as an improvement operator for a model-free policy, matching or exceeding top model-free methods across both continuous and discrete environments.