5 citations · 10 across the 3 of their papers we have counts for
4 papers · 1 filter
An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning
Astrid Vanneste, Simon Vanneste, Kevin Mets +4
Communication is crucial in multi-agent reinforcement learning when agents are not able to observe the full state of the environment. The most common approach to allow learned comm…
Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System
Simon Vanneste, Gauthier de Borrekens, Stig Bosmans +5
Recent work in multi-agent reinforcement learning has investigated inter agent communication which is learned simultaneously with the action policy in order to improve the team rew…
Mixed Cooperative-Competitive Communication Using Multi-Agent Reinforcement Learning
Astrid Vanneste, Wesley Van Wijnsberghe, Simon Vanneste +4
By using communication between multiple agents in multi-agent environments, one can reduce the effects of partial observability by combining one agent's observation with that of ot…
Exploiting non-i.i.d. data towards more robust machine learning algorithms
Wim Casteels, Peter Hellinckx
In the field of machine learning there is a growing interest towards more robust and generalizable algorithms. This is for example important to bridge the gap between the environme…