7 citations · 23 across the 8 of their papers we have counts for
9 papers
Safety Aware Autonomous Path Planning Using Model Predictive Reinforcement Learning for Inland Waterways
Astrid Vanneste, Simon Vanneste, Olivier Vasseur +7
In recent years, interest in autonomous shipping in urban waterways has increased significantly due to the trend of keeping cars and trucks out of city centers. Classical approache…
An In-Depth Analysis of Discretization Methods for Communication Learning using Backpropagation with Multi-Agent Reinforcement Learning
Astrid Vanneste, Simon Vanneste, Kevin Mets +3
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…
Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning
Astrid Vanneste, Thomas Somers, Simon Vanneste +4
Many multi-agent systems require inter-agent communication to properly achieve their goal. By learning the communication protocol alongside the action protocol using multi-agent re…
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…
A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark
Amber Cassimon, Reinout Eyckerman, Siegfried Mercelis +2
In this paper, the authors investigate the Deep Sea Treasure (DST) problem as proposed by Vamplew et al. Through a number of proofs, the authors show the original DST problem to be…
Learning to Communicate Using Counterfactual Reasoning
Simon Vanneste, Astrid Vanneste, Kevin Mets +5
Learning to communicate in order to share state information is an active problem in the area of multi-agent reinforcement learning (MARL). The credit assignment problem, the non-st…
Towards evaluating emergent behavior of the Internet of Things using large scale simulation techniques
Stig Bosmans, Siegfried Mercelis Peter Hellinckx, Joachim Denil
With the increase in Internet of Things devices and more decentralized architectures we see a new type of application gain importance, a type where local interactions between indiv…