Search Results for author:
Found 10 papers, 3 papers with code
Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial Policies
Optimal control of parametric partial differential equations (PDEs) is crucial in many applications in engineering and science.
CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning
Causal discovery is the challenging task of inferring causal structure from data.
Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
This work proposes a Stochastic Variational Deep Kernel Learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data.
Unsupervised Representation Learning in Deep Reinforcement Learning: A Review
This review addresses the problem of learning abstract representations of the measurement data in the context of Deep Reinforcement Learning (DRL).
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning
Inspection and maintenance are two crucial aspects of industrial pipeline plants.
Autonomous Navigation
Hierarchical Reinforcement Learning
+2
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics
Results show that the novel framework can efficiently learn low-dimensional and interpretable state and action representations and the optimal latent policy.
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
Autonomous robots require high degrees of cognitive and motoric intelligence to come into our everyday life.
Low Dimensional State Representation Learning with Reward-shaped Priors
We propose a method that aims at learning a mapping from the observations into a lower-dimensional state space.
On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach
We present a map-less path planning algorithm based on Deep Reinforcement Learning (DRL) for mobile robots navigating in unknown environment that only relies on 40-dimensional raw laser data and odometry information.
Sequential image processing methods for improving semantic video segmentation algorithms
Herein we propose two sequential probabilistic video frame analysis approaches to improve the segmentation performance of the existing algorithms.
