Philipp Altmann | DeepAI

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∙ 04/26/2023

CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing

The safe application of reinforcement learning (RL) requires generalizat...

0 Philipp Altmann, et al. ∙

∙ 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...

0 Philipp Altmann, et al. ∙

∙ 01/18/2023

Learning to Participate through Trading of Reward Shares

Enabling autonomous agents to act cooperatively is an important step to ...

0 Michael Kölle, et al. ∙

∙ 01/06/2023

SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training

Applying new computing paradigms like quantum computing to the field of ...

0 Philipp Altmann, et al. ∙

∙ 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...

0 Fabian Ritz, et al. ∙

∙ 09/22/2021

Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines

Reinforcement learning has driven impressive advances in machine learnin...

0 Tobias Müller, et al. ∙

∙ 08/08/2019

Benchmarking Surrogate-Assisted Genetic Recommender Systems

We propose a new approach for building recommender systems by adapting s...

2 Thomas Gabor, et al. ∙

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