Deep reinforcement learning for cyber security
The scale of Internet-connected systems has increased considerably, and these systems are
being exposed to cyberattacks more than ever. The complexity and dynamics of …
being exposed to cyberattacks more than ever. The complexity and dynamics of …
Learning task-state representations
Y Niv - Nature neuroscience, 2019 - nature.com
Arguably, the most difficult part of learning is deciding what to learn about. Should I
associate the positive outcome of safely completing a street-crossing with the situation 'the …
associate the positive outcome of safely completing a street-crossing with the situation 'the …
Learning heuristics for the tsp by policy gradient
The aim of the study is to provide interesting insights on how efficient machine learning
algorithms could be adapted to solve combinatorial optimization problems in conjunction …
algorithms could be adapted to solve combinatorial optimization problems in conjunction …
Topology optimization via machine learning and deep learning: A review
S Shin, D Shin, N Kang - Journal of Computational Design and …, 2023 - academic.oup.com
Topology optimization (TO) is a method of deriving an optimal design that satisfies a given
load and boundary conditions within a design domain. This method enables effective design …
load and boundary conditions within a design domain. This method enables effective design …
Latent learning, cognitive maps, and curiosity
Curiosity is a desire for information that is not motivated by strategic concerns. Latent
learning is not driven by standard reinforcement processes. We propose that curiosity serves …
learning is not driven by standard reinforcement processes. We propose that curiosity serves …
Deep learning for cognitive neuroscience
KR Storrs, N Kriegeskorte - arXiv preprint arXiv:1903.01458, 2019 - arxiv.org
Neural network models can now recognise images, understand text, translate languages,
and play many human games at human or superhuman levels. These systems are highly …
and play many human games at human or superhuman levels. These systems are highly …
Deep hierarchical reinforcement learning for autonomous driving with distinct behaviors
J Chen, Z Wang, M Tomizuka - 2018 IEEE intelligent vehicles …, 2018 - ieeexplore.ieee.org
Deep reinforcement learning has achieved great progress recently in domains such as
learning to play Atari games from raw pixel input. The model-free characteristics of …
learning to play Atari games from raw pixel input. The model-free characteristics of …
Exploring the promise and limits of real-time recurrent learning
Real-time recurrent learning (RTRL) for sequence-processing recurrent neural networks
(RNNs) offers certain conceptual advantages over backpropagation through time (BPTT) …
(RNNs) offers certain conceptual advantages over backpropagation through time (BPTT) …
Behavioral priors and dynamics models: Improving performance and domain transfer in offline rl
Offline Reinforcement Learning (RL) aims to extract near-optimal policies from imperfect
offline data without additional environment interactions. Extracting policies from diverse …
offline data without additional environment interactions. Extracting policies from diverse …
Reliability-optimal offloading in low-latency edge computing networks: Analytical and reinforcement learning based designs
In this paper, we consider a multi-access edge computing (MEC) network with multiple
servers. Due to the low latency constraints, the wireless data transmission/offloading is …
servers. Due to the low latency constraints, the wireless data transmission/offloading is …