Search Results for author: Frederik Schubert

Found 8 papers, 5 papers with code

Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games

no code implementations5 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.

POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning

no code implementations23 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.

Open-Ended Question Answering reinforcement-learning +2

Contextualize Me -- The Case for Context in Reinforcement Learning

1 code implementation9 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.

reinforcement-learning Reinforcement Learning (RL) +1

CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning

1 code implementation5 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.

Physical Simulations reinforcement-learning +2

World-GAN: a Generative Model for Minecraft Worlds

1 code implementation18 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.

Generative Adversarial Network

Automatic Risk Adaptation in Distributional Reinforcement Learning

no code implementations11 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

TOAD-GAN: Coherent Style Level Generation from a Single Example

2 code implementations4 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.

Generative Adversarial Network

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