In this paper, we consider the problem of adjusting the exploration rate...
Conventional information-theoretic quantities assume access to probabili...
We propose a memory-based framework for real-time, data-efficient target...
A fundamental problem when aggregating Markov chains is the specificatio...
Reinforcement learning in large-scale environments is challenging due to...
Deep-predictive-coding networks (DPCNs) are hierarchical, generative mod...
We propose a saliency-based, multi-target detection and segmentation
fra...
In this paper, we propose an approach to obtain reduced-order models of
...
Radial-basis-function networks are traditionally defined for sets of
vec...
Reinforcement learning in environments with many action-state pairs is
c...
In this paper, we provide an approach to clustering relational matrices ...
In this paper, we propose an information-theoretic exploration strategy ...
Conventional reinforcement learning methods for Markov decision processe...