Results 21 to 30 of about 298,347 (266)
Scenario-assisted Deep Reinforcement Learning [PDF]
Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development, 2022 Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (
Yerushalmi, Raz +5 more
openaire +2 more sources
Explainability in deep reinforcement learning [PDF]
Knowledge-Based Systems, 2021 Article accepted at Knowledge-Based ...
Heuillet, Alexandre +2 more
openaire +3 more sources
World Electric Vehicle Journal, 2023 Given the influence of the randomness of driving conditions on the energy management strategy of vehicles, deep reinforcement learning considering driving conditions prediction was proposed.
Jianguo Xi +3 more
doaj +1 more source
Deep reinforcement learning of transition states [PDF]
Physical Chemistry Chemical Physics, 2021 RL‡can automatically locate the transition states of chemical reactions through deep reinforcement learning of feedback from molecular simulations.
Jun Zhang +7 more
openaire +3 more sources
Tongxin xuebao, 2023 To solve the problem that intelligent devices equipped with deep reinforcement learning agents lack effective security data sharing mechanisms in the intelligent Internet of things, a general federated reinforcement learning (GenFedRL) framework was ...
Biao JIN +4 more
doaj +2 more sources
Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review
Robotics, 2021 Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence ...
Rongrong Liu +4 more
doaj +1 more source
Deep Reinforcement Learning in Medicine [PDF]
Kidney Diseases, 2018 Reinforcement learning has achieved tremendous success in recent years, notably in complex games such as Atari, Go, and chess. In large part, this success has been made possible by powerful function approximation methods in the form of deep neural networks.
openaire +3 more sources
Abstraction for Deep Reinforcement Learning
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 We characterise the problem of abstraction in the context of deep reinforcement learning. Various well established approaches to analogical reasoning and associative memory might be brought to bear on this issue, but they present difficulties because of the need for end-to-end differentiability.
Shanahan, M, Mitchell, M
openaire +3 more sources
Restoring chaos using deep reinforcement learning [PDF]
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2020 A catastrophic bifurcation in non-linear dynamical systems, called crisis, often leads to their convergence to an undesirable non-chaotic state after some initial chaotic transients. Preventing such behavior has been quite challenging. We demonstrate that deep Reinforcement Learning (RL) is able to restore chaos in a transiently chaotic regime of the ...
Sumit Vashishtha, Siddhartha Verma
openaire +3 more sources
Deep Reinforcement Learning for Multiobjective Optimization [PDF]
IEEE Transactions on Cybernetics, 2021 This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), that we call DRL-MOA. The idea of decomposition is adopted to decompose the MOP into a set of scalar optimization subproblems. Then each subproblem is modelled as a neural network.
Kaiwen Li, Tao Zhang, Rui Wang
openaire +3 more sources