1 citations · 1 across the 5 of their papers we have counts for
5 papers
Monotone Optimisation with Learned Projections
Ahmed Rashwan, Keith Briggs, Chris Budd +1
Monotone optimisation problems admit specialised global solvers such as the Polyblock Outer Approximation (POA) algorithm, but these methods typically require explicit objective an…
Factored Value Functions for Graph-Based Multi-Agent Reinforcement Learning
Ahmed Rashwan, Keith Briggs, Chris Budd +1
Credit assignment is a core challenge in multi-agent reinforcement learning (MARL), especially in large-scale systems with structured, local interactions. Graph-based Markov decisi…
Enforcing convex constraints in Graph Neural Networks
Ahmed Rashwan, Keith Briggs, Chris Budd +1
Many machine learning applications require outputs that satisfy complex, dynamic constraints. This task is particularly challenging in Graph Neural Network models due to the variab…
A neural drift-plus-penalty algorithm for network power allocation and routing
Ahmed Rashwan, Keith Briggs, Chris Budd
The drift-plus-penalty method is a Lyapunov optimisation technique commonly applied to network routing problems. It reduces the original stochastic planning task to a sequence of g…
AI-Ready Energy Modelling for Next Generation RAN
Kishan Sthankiya, Keith Briggs, Mona Jaber +1
Recent sustainability drives place energy-consumption metrics in centre-stage for the design of future radio access networks (RAN). At the same time, optimising the trade-off betwe…