Results 21 to 30 of about 1,629,210 (315)
Weakly Coupled Deep Q-Networks
, 2023 We propose weakly coupled deep Q-networks (WCDQN), a novel deep reinforcement learning algorithm that enhances performance in a class of structured problems called weakly coupled Markov decision processes (WCMDP). WCMDPs consist of multiple independent subproblems connected by an action space constraint, which is a structural property that frequently ...
Shar, Ibrahim El, Jiang, Daniel R.
openaire +2 more sources
Autonomous Penetration Testing Based on Improved Deep Q-Network
Applied Sciences, 2021 Penetration testing is an effective way to test and evaluate cybersecurity by simulating a cyberattack. However, the traditional methods deeply rely on domain expert knowledge, which requires prohibitive labor and time costs.
Shicheng Zhou +4 more
semanticscholar +1 more source
Deep Q‐network implementation for simulated autonomous vehicle control
IET Intelligent Transport Systems, 2021 Deep reinforcement learning is poised to be a revolutionised step towards newer possibilities in solving navigation and autonomous vehicle control tasks.
Yang Thee Quek +4 more
doaj +1 more source
Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning [PDF]
, 2018 This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles.
Ali, Mohammad +4 more
core +2 more sources
The use of adversaries for optimal neural network training [PDF]
, 2018 B-decay data from the Belle experiment at the KEKB collider have a substantial background from $e^{+}e^{-}\to q \bar{q}$ events. To suppress this we employ deep neural network algorithms.
Hawthorne-Gonzalvez, Anton +1 more
core +2 more sources
B-APFDQN: A UAV Path Planning Algorithm Based on Deep Q-Network and Artificial Potential Field
IEEE Access, 2023 Deep Q-network (DQN) is one of the standard methods to solve the Unmanned Aerial Vehicle (UAV) path planning problem. However, the way agent deepens its cognition of the environment through frequent random trial-and-error leads to slow convergence.
Fuchen Kong +3 more
semanticscholar +1 more source
Visual servoing with deep reinforcement learning for rotor unmanned helicopter
International Journal of Advanced Robotic Systems, 2022 Visual servoing is a key approach to achieve visual control for the rotor unmanned helicopter. The challenges of the inaccurate matrix estimation and the target loss restrict the performance of the visual servoing control systems.
Chunyang Hu, Wenping Cao, Bin Ning
doaj +1 more source
Deep Reinforcement Learning- based load balancing strategy for multiple controllers in SDN
e-Prime: Advances in Electrical Engineering, Electronics and Energy, 2022 In Software-Defined Network (SDN) with multiple controllers, static mapping relationship between switches and controllers may cause some controllers to be overloaded, while some controller resources are underutilized.
Min Xiang +3 more
doaj +1 more source
Distribution Network Reconfiguration Based on NoisyNet Deep Q-Learning Network
IEEE Access, 2021 The distribution network reconfiguration (DNR) aims at minimizing the power losses and improving the voltage profile. Traditional model-based methods exactly need the network parameters to derive the optimal configuration of the distribution network ...
Beibei Wang +4 more
doaj +1 more source
IEEE Access, 2023 In this research, a novel navigation algorithm for self-driving vehicles that avoids collisions with pedestrians and ad hoc obstacles is described. The proposed algorithm predicts the locations of ad hoc obstacles and wandering pedestrians by using an ...
N. S. Manikandan +2 more
doaj +1 more source