Search Results for author: Roland Hafner

Found 16 papers, 1 papers with code

Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration

no code implementations17 Sep 2021 Oliver Groth, Markus Wulfmeier, Giulia Vezzani, Vibhavari Dasagi, Tim Hertweck, Roland Hafner, Nicolas Heess, Martin Riedmiller

Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks.

Collect & Infer -- a fresh look at data-efficient Reinforcement Learning

no code implementations23 Aug 2021 Martin Riedmiller, Jost Tobias Springenberg, Roland Hafner, Nicolas Heess

This position paper proposes a fresh look at Reinforcement Learning (RL) from the perspective of data-efficiency.

reinforcement-learning

Simple Sensor Intentions for Exploration

no code implementations15 May 2020 Tim Hertweck, Martin Riedmiller, Michael Bloesch, Jost Tobias Springenberg, Noah Siegel, Markus Wulfmeier, Roland Hafner, Nicolas Heess

In particular, we show that a real robotic arm can learn to grasp and lift and solve a Ball-in-a-Cup task from scratch, when only raw sensor streams are used for both controller input and in the auxiliary reward definition.

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

no code implementations2 Jan 2020 Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Jost Tobias Springenberg, Roland Hafner, Francesco Romano, Jonas Buchli, Nicolas Heess, Martin Riedmiller

In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously.

reinforcement-learning

PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations

no code implementations27 May 2017 Rico Jonschkowski, Roland Hafner, Jonathan Scholz, Martin Riedmiller

We propose position-velocity encoders (PVEs) which learn---without supervision---to encode images to positions and velocities of task-relevant objects.

Image Reconstruction

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