16 citations · 77 across the 21 of their papers we have counts for
5 papers · 1 filter
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning
Andreas Sedlmeier, Thomas Gabor, Thomy Phan +2
Robustness to out-of-distribution (OOD) data is an important goal in building reliable machine learning systems. Especially in autonomous systems, wrong predictions for OOD inputs…
Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning
Carsten Hahn, Thomy Phan, Thomas Gabor +2
In nature, flocking or swarm behavior is observed in many species as it has beneficial properties like reducing the probability of being caught by a predator. In this paper, we pro…
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…
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning
Andreas Sedlmeier, Thomas Gabor, Thomy Phan +2
We consider the problem of detecting out-of-distribution (OOD) samples in deep reinforcement learning. In a value based reinforcement learning setting, we propose to use uncertaint…