activity
20152021
most citedAnalysing Congestion Problems in Multi-agent Reinforcement Learning

6 citations · 21 across the 8 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

q-bio.PE2019

Towards a phylogenetic measure to quantify HIV incidence

Pieter Libin, Nassim Versbraegen, Ana B. Abecasis +3

One of the cornerstones in combating the HIV pandemic is being able to assess the current state and evolution of local HIV epidemics. This remains a complex problem, as many HIV in…

cs.CV20194 cited

IPC-Net: 3D point-cloud segmentation using deep inter-point convolutional layers

Felipe Gomez Marulanda, Pieter Libin, Timothy Verstraeten +1

Over the last decade, the demand for better segmentation and classification algorithms in 3D spaces has significantly grown due to the popularity of new 3D sensor technologies and…

cs.MA2019

Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey

Roxana Rădulescu, Patrick Mannion, Diederik M. Roijers +1

The majority of multi-agent system (MAS) implementations aim to optimise agents' policies with respect to a single objective, despite the fact that many real-world problem domains…

cs.AI20191 cited

Transfer Learning Across Simulated Robots With Different Sensors

Hélène Plisnier, Denis Steckelmacher, Diederik Roijers +1

For a robot to learn a good policy, it often requires expensive equipment (such as sophisticated sensors) and a prepared training environment conducive to learning. However, it is…

cs.LG2019

Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics

Denis Steckelmacher, Hélène Plisnier, Diederik M. Roijers +1

Value-based reinforcement-learning algorithms provide state-of-the-art results in model-free discrete-action settings, and tend to outperform actor-critic algorithms. We argue that…

cs.AI20193 cited

The Actor-Advisor: Policy Gradient With Off-Policy Advice

Hélène Plisnier, Denis Steckelmacher, Diederik M. Roijers +1

Actor-critic algorithms learn an explicit policy (actor), and an accompanying value function (critic). The actor performs actions in the environment, while the critic evaluates the…