Results 121 to 130 of about 1,056,132 (188)

Network traffic control method of NHP based on deep reinforcement learning. [PDF]

Sci Rep
Huang Q, Tan Z, Wang Q, Jia Z, Chen B.
europepmc   +1 more source

Adaptive traffic signal control using deep reinforcement learning: Toward smarter and safer urban mobility. [PDF]

PLoS One
Alanazi F, Armghan A, Tanveer M, Yousef A.   +3 more
europepmc   +1 more source

BeamCraft: Deep Reinforcement Learning-DrivenMulti-Objective Beamforming for ISAC

Dao DN, Miao Y.
europepmc   +1 more source

Trustworthy navigation with variational policy in deep reinforcement learning. [PDF]

Front Robot AI
Bockrath K, Ernst L, Nadeem R, Pedraza B, Dera D.   +4 more
europepmc   +1 more source

Deep Reinforcement Learning for Secure and Low-Latency Communications in UAV-Mounted STAR-RIS Assisted Urban Vehicular Networks. [PDF]

Sensors (Basel)
Tang J, Yuan J, Zhao H, Chen M, Peng Y.
europepmc   +1 more source

Deep reinforcement learning for scheduling semiconductor cluster tools in varying configurations. [PDF]

Sci Rep
Choi J, Kim SB.
europepmc   +1 more source

Perception-Aware Cooperative Path Planning for Multi-UAV Systems in Urban Wind Fields via Deep Reinforcement Learning. [PDF]

Sensors (Basel)
Ding J, Wang L, Jin S, Wang D.
europepmc   +1 more source

Temporal influence maximization via continuous-time graph neural networks and deep reinforcement learning. [PDF]

Sci Rep
Wang Y, Alawad MA, Alfilh RHC, Singh Sawaran Singh N.   +3 more
europepmc   +1 more source

AI-driven hybrid framework for enhanced pest detection and resource optimization using graph networks and deep reinforcement learning. [PDF]

Sci Rep
Shivahare BD, Singh G, Naz R, Yadav SA, Mathivanan SK, S K B S.   +5 more
europepmc   +1 more source

MMU-STCNN-BDQ: a deep reinforcement learning framework for secure and energy-efficient beamforming in 6G mMIMO networks. [PDF]

Sci Rep
Ramudu K, Medasani S, Addepalli T, Sharma M, Pandey A, Srinubabu M.   +5 more
europepmc   +1 more source