Gerhard Neumann

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University of Lincoln

Differentiable Trust Region Layers for Deep Reinforcement Learning

Trust region methods are a popular tool in reinforcement learning as the...
01/22/2021 ∙ by Fabian Otto, et al. ∙ 0 ∙ share

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Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning

Estimating accurate forward and inverse dynamics models is a crucial com...
10/20/2020 ∙ by Vaisakh Shaj, et al. ∙ 14 ∙ share

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Imitation Learning for Autonomous Trajectory Learning of Robot Arms in Space

This work adds on to the on-going efforts to provide more autonomy to sp...
08/10/2020 ∙ by RB Ashith Shyam, et al. ∙ 0 ∙ share

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Non-Adversarial Imitation Learning and its Connections to Adversarial Methods

Many modern methods for imitation learning and inverse reinforcement lea...
08/08/2020 ∙ by Oleg Arenz, et al. ∙ 0 ∙ share

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Probabilistic approach to physical object disentangling

Physically disentangling entangled objects from each other is a problem ...
02/26/2020 ∙ by Joni Pajarinen, et al. ∙ 0 ∙ share

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Expected Information Maximization: Using the I-Projection for Mixture Density Estimation

Modelling highly multi-modal data is a challenging problem in machine le...
01/23/2020 ∙ by Philipp Becker, et al. ∙ 0 ∙ share

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Trust-Region Variational Inference with Gaussian Mixture Models

Many methods for machine learning rely on approximate inference from int...
07/10/2019 ∙ by Oleg Arenz, et al. ∙ 5 ∙ share

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Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces

In order to integrate uncertainty estimates into deep time-series modell...
05/17/2019 ∙ by Philipp Becker, et al. ∙ 0 ∙ share

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Compatible Natural Gradient Policy Search

Trust-region methods have yielded state-of-the-art results in policy sea...
02/07/2019 ∙ by Joni Pajarinen, et al. ∙ 0 ∙ share

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An Algorithmic Perspective on Imitation Learning

As robots and other intelligent agents move from simple environments and...
11/16/2018 ∙ by Takayuki Osa, et al. ∙ 0 ∙ share

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Adaptation and Robust Learning of Probabilistic Movement Primitives

Probabilistic representations of movement primitives open important new ...
08/31/2018 ∙ by Sebastián Gómez-González, et al. ∙ 0 ∙ share

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Towards Fine Grained Network Flow Prediction

One main challenge for the design of networks is that traffic load is no...
08/20/2018 ∙ by Patrick Jahnke, et al. ∙ 0 ∙ share

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Deep Reinforcement Learning for Swarm Systems

Recently, deep reinforcement learning (RL) methods have been applied suc...
07/17/2018 ∙ by Maximilian Hüttenrauch, et al. ∙ 0 ∙ share

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Learning Complex Swarm Behaviors by Exploiting Local Communication Protocols with Deep Reinforcement Learning

Swarm systems constitute a challenging problem for reinforcement learnin...
09/21/2017 ∙ by Maximilian Hüttenrauch, et al. ∙ 0 ∙ share

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Guided Deep Reinforcement Learning for Swarm Systems

In this paper, we investigate how to learn to control a group of coopera...
09/18/2017 ∙ by Maximilian Hüttenrauch, et al. ∙ 0 ∙ share

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Policy Search with High-Dimensional Context Variables

Direct contextual policy search methods learn to improve policy paramete...
11/10/2016 ∙ by Voot Tangkaratt, et al. ∙ 0 ∙ share

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Gerhard Neumann

Featured Co-authors

Masashi Sugiyama

Pieter Abbeel

Bernhard Schölkopf

Jan Peters

J Andrew Bagnell

Herke van Hoof

Voot Tangkaratt

Joni Pajarinen

Takayuki Osa

Cheng Zhao

Mingjun Zhong

Differentiable Trust Region Layers for Deep Reinforcement Learning

Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning

Imitation Learning for Autonomous Trajectory Learning of Robot Arms in Space

Non-Adversarial Imitation Learning and its Connections to Adversarial Methods

Probabilistic approach to physical object disentangling

Expected Information Maximization: Using the I-Projection for Mixture Density Estimation

Trust-Region Variational Inference with Gaussian Mixture Models

Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces

Compatible Natural Gradient Policy Search

An Algorithmic Perspective on Imitation Learning

Adaptation and Robust Learning of Probabilistic Movement Primitives

Towards Fine Grained Network Flow Prediction

Deep Reinforcement Learning for Swarm Systems

Learning Complex Swarm Behaviors by Exploiting Local Communication Protocols with Deep Reinforcement Learning

Guided Deep Reinforcement Learning for Swarm Systems

Policy Search with High-Dimensional Context Variables