152 citations · 297 across the 6 of their papers we have counts for
7 papers
Correlation Priors for Reinforcement Learning
Bastian Alt, Adrian Šošić, Heinz Koeppl
Many decision-making problems naturally exhibit pronounced structures inherited from the characteristics of the underlying environment. In a Markov decision process model, for exam…
Deep Reinforcement Learning for Swarm Systems
Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann
Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to repr…
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling
Adrian Šošić, Elmar Rueckert, Jan Peters +2
Advances in the field of inverse reinforcement learning (IRL) have led to sophisticated inference frameworks that relax the original modeling assumption of observing an agent behav…
Guided Deep Reinforcement Learning for Swarm Systems
Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann
In this paper, we investigate how to learn to control a group of cooperative agents with limited sensing capabilities such as robot swarms. The agents have only very basic sensor c…
Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning
Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann
Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensi…
A Bayesian Approach to Policy Recognition and State Representation Learning
Adrian Šošić, Abdelhak M. Zoubir, Heinz Koeppl
Learning from demonstration (LfD) is the process of building behavioral models of a task from demonstrations provided by an expert. These models can be used e.g. for system control…
Inverse Reinforcement Learning in Swarm Systems
Adrian Šošić, Wasiur R. KhudaBukhsh, Abdelhak M. Zoubir +1
Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data. However, IRL remains mostly unexplored for multi-agent systems…