1 code implementation • 9 Mar 2021 • Mario Coppola, Jian Guo, Eberhard Gill, Guido C. H. E. de Croon
The framework is based on the automatic extraction of two distinct models: 1) a neural network model trained to estimate the relationship between the robots' sensor readings and the global performance of the swarm, and 2) a probabilistic state transition model that explicitly models the local state transitions (i. e., transitions in observations from the perspective of a single robot in the swarm) given a policy.
1 code implementation • 5 Mar 2021 • Daniël Willemsen, Mario Coppola, Guido C. H. E. de Croon
MAMBPO uses a learned world model to improve sample efficiency compared to model-free Multi-Agent Soft Actor-Critic (MASAC).
1 code implementation • 12 Mar 2020 • Shushuai Li, Mario Coppola, Christophe De Wagter, Guido C. H. E. de Croon
Accurate relative localization is an important requirement for a swarm of robots, especially when performing a cooperative task.
Robotics Multiagent Systems
no code implementations • 18 Apr 2018 • Mario Coppola, Jian Guo, Eberhard K. A. Gill, Guido C. H. E. de Croon
We then formally show that these local states can only coexist when the global desired pattern is achieved and that, until this occurs, there is always a sequence of actions that will lead from the current pattern to the desired pattern.
Robotics