5 citations · 14 across the 6 of their papers we have counts for
6 papers
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…
HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal Memory
Jakob Struye, Kevin Mets, Steven Latré
Building Reinforcement Learning (RL) algorithms which are able to adapt to continuously evolving tasks is an open research challenge. One technology that is known to inherently han…
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning
Matthias Hutsebaut-Buysse, Kevin Mets, Steven Latré
Reinforcement learning (RL) algorithms typically start tabula rasa, without any prior knowledge of the environment, and without any prior skills. This however often leads to low sa…
Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning
Matthias Hutsebaut-Buysse, Kevin Mets, Steven Latré
Over its lifetime, a reinforcement learning agent is often tasked with different tasks. How to efficiently adapt a previously learned control policy from one task to another, remai…