Dynatrace banner image
 Marius-Constantin Dinu

Marius-Constantin Dinu

Senior Researcher

Marius was part of the research lab from 2022 until 2023, working in the field of Machine Learning and Artificial Intelligence. 

 

Authored publications

A Dataset Perspective on Offline Reinforcement Learning

The application of Reinforcement Learning (RL) in real world environments can be expensive or risky due to sub-optimal policies during training. In Offline RL, this problem is avoided since interactions with an environment are prohibited. Policies are learned from a given dataset, which solely determines their performance. Despite this fact, how da...

Kajetan SchweighoferAndreas RadlerMarius-Constantin DinuMarkus HofmarcherVihang PatilAngela Bitto-NemlingHamid Eghbal-zadehSepp Hochreiter

| 2022

Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

Reinforcement Learning algorithms require a large number of samples to solve complex tasks with sparse and delayed rewards. Complex tasks can often be hierarchically decomposed into sub-tasks. A step in the Q-function can be associated with solving a sub-task, where the expectation of the return increases. RUDDER has been introduced to identify the...

Vihang P. Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M. Blies, Johannes Brandstetter, Jose A. Arjona-Medina, Sepp Hochreiter

| Machine Learning, arXiv:2009.14108 | 2022

Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning

In lifelong learning, an agent learns throughout its entire life without resets, in a constantly changing environment, as we humans do. Consequently, lifelong learning comes with a plethora of research problems such as continual domain shifts, which result in non-stationary rewards and environment dynamics. These non-stationarities are difficult to...

Christian SteinparzThomas SchmiedFabian PaischerMarius-Constantin DinuVihang PatilAngela Bitto-NemlingHamid Eghbal-zadehSepp Hochreiter

| 2022

Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning

In real world, affecting the environment by a weak policy can be expensive or very risky, therefore hampers real world applications of reinforcement learning. Offline Reinforcement Learning (RL) can learn policies from a given dataset without interacting with the environment. However, the dataset is the only source of information for an Offline RL...

Kajetan Schweighofer, Markus Hofmarcher, Marius-Constantin Dinu, Philipp Renz, Angela Bitto-Nemling, Vihang Patil, and Sepp Hochreiter

| NeurIPS Workshop | 2022

The balancing principle for parameter choice in distance-regularized domain adaptation

We address the unsolved algorithm design problem of choosing a justified regularization parameter in unsupervised domain adaptation. This problem is intriguing as no labels are available in the target domain. Our approach starts with the observation that the widely-used method of minimizing the source error, penalized by a distance measure between ...

Werner Zellinger, Natalia Shepeleva, Marius-Constantin Dinu, Hamid Eghbal-zadeh, Hoan Duc Nguyen, Bernhard Nessler, Sergei Pereverzyev, Bernhard A. Moser

| Advances in Neural Information Processing Systems 34 (NeurIPS 2021) | 2021