5 citations · 5 across the 6 of their papers we have counts for
7 papers
Light-Weight Diffusion Multiplier and Uncertainty Quantification for Fourier Neural Operators
Albert Matveev, Sanmitra Ghosh, Aamal Hussain +2
Operator learning is a powerful paradigm for solving partial differential equations, with Fourier Neural Operators serving as a widely adopted foundation. However, FNOs face signif…
Convergence and Connectivity: Dynamics of Multi-Agent Q-Learning in Random Networks
Dan Leonte, Aamal Hussain, Raphael Huser +2
Beyond specific settings, many multi-agent learning algorithms fail to converge to an equilibrium solution, instead displaying complex, non-stationary behaviours such as recurrent…
On the Stability of Learning in Network Games with Many Players
Aamal Hussain, Dan Leonte, Francesco Belardinelli +1
Multi-agent learning algorithms have been shown to display complex, unstable behaviours in a wide array of games. In fact, previous works indicate that convergent behaviours are le…
Stability of Multi-Agent Learning in Competitive Networks: Delaying the Onset of Chaos
Aamal Hussain, Francesco Belardinelli
The behaviour of multi-agent learning in competitive network games is often studied within the context of zero-sum games, in which convergence guarantees may be obtained. However,…
Beyond Strict Competition: Approximate Convergence of Multi Agent Q-Learning Dynamics
Aamal Hussain, Francesco Belardinelli, Georgios Piliouras
The behaviour of multi-agent learning in competitive settings is often considered under the restrictive assumption of a zero-sum game. Only under this strict requirement is the beh…
Stability of Multi-Agent Learning: Convergence in Network Games with Many Players
Aamal Hussain, Dan Leonte, Francesco Belardinelli +1
The behaviour of multi-agent learning in many player games has been shown to display complex dynamics outside of restrictive examples such as network zero-sum games. In addition, i…
Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamics
Aamal Abbas Hussain, Francesco Belardinelli, Georgios Piliouras
Achieving convergence of multiple learning agents in general -player games is imperative for the development of safe and reliable machine learning (ML) algorithms and their appl…