Search Results for author: Artemij Amiranashvili

Found 8 papers, 5 papers with code

Pre-training of Deep RL Agents for Improved Learning under Domain Randomization

no code implementations29 Apr 2021 Artemij Amiranashvili, Max Argus, Lukas Hermann, Wolfram Burgard, Thomas Brox

Visual domain randomization in simulated environments is a widely used method to transfer policies trained in simulation to real robots.

reinforcement-learning Reinforcement Learning (RL)

Scaling Imitation Learning in Minecraft

1 code implementation6 Jul 2020 Artemij Amiranashvili, Nicolai Dorka, Wolfram Burgard, Vladlen Koltun, Thomas Brox

Imitation learning is a powerful family of techniques for learning sensorimotor coordination in immersive environments.

Data Augmentation Imitation Learning

Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control

1 code implementation17 Oct 2019 Lukas Hermann, Max Argus, Andreas Eitel, Artemij Amiranashvili, Wolfram Burgard, Thomas Brox

We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for reinforcement learning in the presence of sparse rewards.

Reinforcement Learning (RL)

Motion Perception in Reinforcement Learning with Dynamic Objects

no code implementations10 Jan 2019 Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun, Thomas Brox

In dynamic environments, learned controllers are supposed to take motion into account when selecting the action to be taken.

Continuous Control reinforcement-learning +1

TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning

1 code implementation ICLR 2018 Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun, Thomas Brox

Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using tabular representations and linear function approximators.

Reinforcement Learning (RL)

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