191 citations · 551 across the 134 of their papers we have counts for
7 papers · 1 filter
Optimization of Link Configuration for Satellite Communication Using Reinforcement Learning
Tobias Rohe, Michael Kölle, Jan Matheis +3
Satellite communication is a key technology in our modern connected world. With increasingly complex hardware, one challenge is to efficiently configure links (connections) on a sa…
Swarm Behavior Cloning
Jonas Nüßlein, Maximilian Zorn, Philipp Altmann +1
In sequential decision-making environments, the primary approaches for training agents are Reinforcement Learning (RL) and Imitation Learning (IL). Unlike RL, which relies on model…
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms
Michael Kölle, Yannick Erpelding, Fabian Ritz +3
Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments. In particular, the predator-pre…
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering
Robert Müller, Hasan Turalic, Thomy Phan +3
In the realm of Multi-Agent Reinforcement Learning (MARL), prevailing approaches exhibit shortcomings in aligning with human learning, robustness, and scalability. Addressing this,…
Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning
Thomy Phan, Thomas Gabor, Robert Müller +2
We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general memory bounded approach to partially observable open-loop planning. SYMBOL maintains an adaptive stack o…
Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling
Thomy Phan, Lenz Belzner, Marie Kiermeier +3
State-of-the-art approaches to partially observable planning like POMCP are based on stochastic tree search. While these approaches are computationally efficient, they may still co…
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies
Thomy Phan, Kyrill Schmid, Lenz Belzner +3
Decision making in multi-agent systems (MAS) is a great challenge due to enormous state and joint action spaces as well as uncertainty, making centralized control generally infeasi…