no code implementations • 5 Feb 2024 • Yannik Mahlau, Frederik Schubert, Bodo Rosenhahn
The combination of self-play and planning has achieved great successes in sequential games, for instance in Chess and Go.
no code implementations • 23 May 2022 • Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
The goal of Unsupervised Reinforcement Learning (URL) is to find a reward-agnostic prior policy on a task domain, such that the sample-efficiency on supervised downstream tasks is improved.
1 code implementation • 23 Feb 2022 • Christoph Reinders, Frederik Schubert, Bodo Rosenhahn
In this work, we address the problem of learning deep neural networks on small datasets.
1 code implementation • 9 Feb 2022 • Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
While Reinforcement Learning ( RL) has made great strides towards solving increasingly complicated problems, many algorithms are still brittle to even slight environmental changes.
1 code implementation • 5 Oct 2021 • Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
While Reinforcement Learning has made great strides towards solving ever more complicated tasks, many algorithms are still brittle to even slight changes in their environment.
1 code implementation • 18 Jun 2021 • Maren Awiszus, Frederik Schubert, Bodo Rosenhahn
This work introduces World-GAN, the first method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example.
no code implementations • 11 Jun 2021 • Frederik Schubert, Theresa Eimer, Bodo Rosenhahn, Marius Lindauer
The use of Reinforcement Learning (RL) agents in practical applications requires the consideration of suboptimal outcomes, depending on the familiarity of the agent with its environment.
Distributional Reinforcement Learning reinforcement-learning +1
2 code implementations • 4 Aug 2020 • Maren Awiszus, Frederik Schubert, Bodo Rosenhahn
In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels.