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Human-level control through deep reinforcement learning

• Volodymyr Mnih, Koray Kavukcuoglu, +16 authors Demis Hassabis
• Nature
• 2015

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an… (More)

Playing Atari with Deep Reinforcement Learning

• Volodymyr Mnih, Koray Kavukcuoglu, +4 authors Martin A. Riedmiller
• ArXiv
• 2013

We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network,… (More)

Striving for Simplicity: The All Convolutional Net

• Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin A. Riedmiller
• ArXiv
• 2014

Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully… (More)

Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method

• Martin A. Riedmiller
• ECML
• 2005

This paper introduces NFQ, an algorithm for efficient and effective training of a Q-value function represented by a multi-layer perceptron. Based on the principle of storing and reusing transition… (More)

Deterministic Policy Gradient Algorithms

• David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin A. Riedmiller
• ICML
• 2014

In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions. The deterministic policy gradient has a particularly appealing form: it is the… (More)

An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems

• Martin Lauer, Martin A. Riedmiller
• ICML
• 2000

Multimodal deep learning for robust RGB-D object recognition

• Andreas Eitel, Jost Tobias Springenberg, Luciano Spinello, Martin A. Riedmiller, Wolfram Burgard
• 2015 IEEE/RSJ International Conference on…
• 2015

Robust object recognition is a crucial ingredient of many, if not all, real-world robotics applications. This paper leverages recent progress on Convolutional Neural Networks (CNNs) and proposes a… (More)

Discriminative Unsupervised Feature Learning with Convolutional Neural Networks

• Alexey Dosovitskiy, Jost Tobias Springenberg, Martin A. Riedmiller, Thomas Brox
• NIPS
• 2014

Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural… (More)

RPROP - A Fast Adaptive Learning Algorithm

• Martin A. Riedmiller, H. Braun
• 1992

A learned feature descriptor for object recognition in RGB-D data

• Manuel Blum, Jost Tobias Springenberg, Jan Wülfing, Martin A. Riedmiller
• 2012 IEEE International Conference on Robotics…
• 2012

In this work we address the problem of feature extraction for object recognition in the context of cameras providing RGB and depth information (RGB-D data). We consider this problem in a bag of… (More)

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