activity
20202023
most citedSafety Aware Autonomous Path Planning Using Model Predictive Reinforcement Learning for Inland Waterways

7 citations · 23 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2023★ 7 cited

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…

cs.LG2023

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…

cs.LG2023★ 1 cited

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…

cs.LG2022★ 3 cited

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…

cs.LG2021★ 2 cited

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…

cs.LG2021★ 5 cited

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…

cs.LG2020★ 5 cited

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…