no code implementations • 31 Jan 2024 • Wei Wei, Tom De Schepper, Kevin Mets
We propose the first continual graph learning benchmark for spatio-temporal graphs and use it to benchmark well-known CGL methods in this novel setting.
no code implementations • 19 Dec 2023 • Steven Mortier, Amir Hamedpour, Bart Bussmann, Ruth Phoebe Tchana Wandji, Steven Latré, Bjarni D. Sigurdsson, Tom De Schepper, Tim Verdonck
Ultimately, this study contributes to our knowledge of the relationships between soil temperature, meteorological variables, and vegetation phenology, providing valuable insights for predicting vegetation phenology characteristics and managing subarctic grasslands in the face of climate change.
no code implementations • 16 Nov 2023 • Astrid Vanneste, Simon Vanneste, Olivier Vasseur, Robin Janssens, Mattias Billast, Ali Anwar, Kevin Mets, Tom De Schepper, Siegfried Mercelis, Peter Hellinckx
We demonstrate our approach on two scenarios and compare the resulting path with path planning using a Frenet frame and path planning based on a proximal policy optimization (PPO) agent.
no code implementations • 9 Aug 2023 • Astrid Vanneste, Simon Vanneste, Kevin Mets, Tom De Schepper, Siegfried Mercelis, Peter Hellinckx
We do this comparison in the context of communication learning using gradients from other agents and perform tests on several environments.
no code implementations • 9 Aug 2023 • Astrid Vanneste, Thomas Somers, Simon Vanneste, Kevin Mets, Tom De Schepper, Siegfried Mercelis, Peter Hellinckx
Therefore, we analyse the communication protocol used by the agents that use the mean message encoder and can conclude that the agents use a combination of an exponential and a logarithmic function in their communication policy to avoid the loss of important information after applying the mean message encoder.
no code implementations • International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2022 • Akash Singh, Tom De Schepper, Kevin Mets, Peter Hellinckx, Jose Oramas, Steven Latre
The proposed method achieves an improvement of around 1. 49% mAP in atomic action recognition and 17. 57% mAP in composite action recognition, over a I3D-NL baseline, on the CATER dataset.
Ranked #1 on Atomic action recognition on CATER (using extra training data)
no code implementations • 15 Nov 2022 • Matthias Hutsebaut-Buysse, Kevin Mets, Tom De Schepper, Steven Latré
Finding an object of a specific class in an unseen environment remains an unsolved navigation problem.
no code implementations • 12 Apr 2022 • Astrid Vanneste, Simon Vanneste, Kevin Mets, Tom De Schepper, Siegfried Mercelis, Steven Latré, Peter Hellinckx
The most common approach to allow learned communication between agents is the use of a differentiable communication channel that allows gradients to flow between agents as a form of feedback.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • Benelux Conference on Artificial Intelligence 2022 • Akash Singh, Tom De Schepper, Kevin Mets, Peter Hellinckx, Jose ́ Oramas, Steven Latre ́
In this paper, we propose DCapsQN, a task-independent CapsNets-based architecture in the deep reinforcement learning setting.
no code implementations • 29 Sep 2021 • Louis Bagot, Kevin Mets, Tom De Schepper, Peter Hellinckx, Steven Latre
As an alternative to the widespread method of a weighted sum of rewards, Explore Options let the agent call an intrinsically motivated agent in order to observe and learn from interesting behaviors in the environment.
no code implementations • 12 Jun 2020 • Simon Vanneste, Astrid Vanneste, Kevin Mets, Tom De Schepper, Ali Anwar, Siegfried Mercelis, Steven Latré, Peter Hellinckx
The credit assignment problem, the non-stationarity of the communication environment and the creation of influenceable agents are major challenges within this research field which need to be overcome in order to learn a valid communication protocol.