Search Results for author: Martin Riedmiller

Found 42 papers, 15 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.

Explicit Pareto Front Optimization for Constrained Reinforcement Learning

no code implementations1 Jan 2021 Sandy Huang, Abbas Abdolmaleki, Philemon Brakel, Steven Bohez, Nicolas Heess, Martin Riedmiller, Raia Hadsell

We propose a framework that uses a multi-objective RL algorithm to find a Pareto front of policies that trades off between the reward and constraint(s), and simultaneously searches along this front for constraint-satisfying policies.

Continuous Control

Representation Matters: Improving Perception and Exploration for Robotics

no code implementations3 Nov 2020 Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, tejas kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin Riedmiller

Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement.

Local Search for Policy Iteration in Continuous Control

no code implementations12 Oct 2020 Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz, Josh Merel, Arunkumar Byravan, Abbas Abdolmaleki, Jackie Kay, Jonas Degrave, Julian Schrittwieser, Yuval Tassa, Jonas Buchli, Dan Belov, Martin Riedmiller

We demonstrate that additional computation spent on model-based policy improvement during learning can improve data efficiency, and confirm that model-based policy improvement during action selection can also be beneficial.

Continuous Control

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.

Quinoa: a Q-function You Infer Normalized Over Actions

no code implementations5 Nov 2019 Jonas Degrave, Abbas Abdolmaleki, Jost Tobias Springenberg, Nicolas Heess, Martin Riedmiller

We present an algorithm for learning an approximate action-value soft Q-function in the relative entropy regularised reinforcement learning setting, for which an optimal improved policy can be recovered in closed form.

Normalising Flows

Robust Reinforcement Learning for Continuous Control with Model Misspecification

no code implementations ICLR 2020 Daniel J. Mankowitz, Nir Levine, Rae Jeong, Yuanyuan Shi, Jackie Kay, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy Mann, Todd Hester, Martin Riedmiller

We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms.

Continuous Control

Relative Entropy Regularized Policy Iteration

1 code implementation5 Dec 2018 Abbas Abdolmaleki, Jost Tobias Springenberg, Jonas Degrave, Steven Bohez, Yuval Tassa, Dan Belov, Nicolas Heess, Martin Riedmiller

Our algorithm draws on connections to existing literature on black-box optimization and 'RL as an inference' and it can be seen either as an extension of the Maximum a Posteriori Policy Optimisation algorithm (MPO) [Abdolmaleki et al., 2018a], or as an extension of Trust Region Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) [Abdolmaleki et al., 2017b; Hansen et al., 1997] to a policy iteration scheme.

Continuous Control OpenAI Gym

Maximum a Posteriori Policy Optimisation

1 code implementation ICLR 2018 Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, Martin Riedmiller

We introduce a new algorithm for reinforcement learning called Maximum aposteriori Policy Optimisation (MPO) based on coordinate ascent on a relative entropy objective.

Continuous Control

Graph networks as learnable physics engines for inference and control

1 code implementation ICML 2018 Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia

Understanding and interacting with everyday physical scenes requires rich knowledge about the structure of the world, represented either implicitly in a value or policy function, or explicitly in a transition model.

DeepMind Control Suite

3 code implementations2 Jan 2018 Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy Lillicrap, Martin Riedmiller

The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents.

Continuous Control

Learning an Embedding Space for Transferable Robot Skills

no code implementations ICLR 2018 Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin Riedmiller

We present a method for reinforcement learning of closely related skills that are parameterized via a skill embedding space.

Variational Inference

Emergence of Locomotion Behaviours in Rich Environments

5 code implementations7 Jul 2017 Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin Riedmiller, David Silver

The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals.

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

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images

1 code implementation NeurIPS 2015 Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller

We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images.

Human level control through deep reinforcement learning

2 code implementations25 Feb 2015 Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg1 & Demis Hassabis

We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters.

Atari Games

Striving for Simplicity: The All Convolutional Net

34 code implementations21 Dec 2014 Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin Riedmiller

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 connected layers.

Image Classification Object Recognition

Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

1 code implementation26 Jun 2014 Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox

While such generic features cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.

General Classification Geometric Matching

Playing Atari with Deep Reinforcement Learning

88 code implementations19 Dec 2013 Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller

We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.

Atari Games Q-Learning

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