research
∙
04/26/2023
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
The safe application of reinforcement learning (RL) requires generalizat...
share
research
∙
01/18/2023
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training
We propose discriminative reward co-training (DIRECT) as an extension to...
research
∙
01/04/2023
Attention-Based Recurrence for Multi-Agent Reinforcement Learning under State Uncertainty
State uncertainty poses a major challenge for decentralized coordination...
research
∙
08/10/2022
Capturing Dependencies within Machine Learning via a Formal Process Model
The development of Machine Learning (ML) models is more than just a spec...
research
∙
12/14/2020
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning
A characteristic of reinforcement learning is the ability to develop unf...
research
∙
08/09/2020
Accelerating Evolutionary Construction Tree Extraction via Graph Partitioning
Extracting a Construction Tree from potentially noisy point clouds is an...
research
∙
04/29/2020
Insights on Training Neural Networks for QUBO Tasks
Current hardware limitations restrict the potential when solving quadrat...
research
∙
04/29/2020
The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline
We discuss the synergetic connection between quantum computing and artif...
research
∙
12/31/2019
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning
Robustness to out-of-distribution (OOD) data is an important goal in bui...
research
∙
07/11/2019
Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning
We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a gene...
research
∙
05/10/2019
Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning
In nature, flocking or swarm behavior is observed in many species as it ...
research
∙
05/10/2019
Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling
State-of-the-art approaches to partially observable planning like POMCP ...
research
∙
03/15/2019
A Quantum Annealing Algorithm for Finding Pure Nash Equilibria in Graphical Games
We introduce Q-Nash, a quantum annealing algorithm for the NP-complete p...
research
∙
01/25/2019
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies
Decision making in multi-agent systems (MAS) is a great challenge due to...
research
∙
01/08/2019
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning
We consider the problem of detecting out-of-distribution (OOD) samples i...
research
∙
10/30/2018
Preparing for the Unexpected: Diversity Improves Planning Resilience in Evolutionary Algorithms
As automatic optimization techniques find their way into industrial appl...
research
∙
04/28/2018
The Sharer's Dilemma in Collective Adaptive Systems of Self-Interested Agents
In collective adaptive systems (CAS), adaptation can be implemented by o...
research
∙
04/17/2018