Results 111 to 120 of about 6,304,901 (198)

Bearing Remaining Useful Life Estimation Using Proximal Policy Optimization (PPO): Validation on the XJTU-SY Run-to-Failure Dataset

International audienceThis study presents a proof-of-concept investigation into the use of proximal policy optimization (PPO), a deep reinforcement learning (DRL) algorithm, for estimating the remaining useful life (RUL) of rolling element bearings ...
Kumar, Shahil, Kumar, Rahul Ranjeev, Cirrincione, Giansalvo   +2 more
core   +1 more source

Comparison of Empirical and Reinforcement Learning (RL)-Based Control Based on Proximal Policy Optimization (PPO) for Walking Assistance: Does AI Always Win? [PDF]

Biomimetics (Basel)
Drewing N, Ahmadi A, Xiong X, Sharbafi MA.   +3 more
europepmc   +1 more source

Proximal policy optimization integrated with Bayesian optimization for stand structure optimization in <i>Pinus yunnanensis</i> secondary forests. [PDF]

Front Plant Sci
Wang J, Lv C, Yin J, Chen Y, Lv S, Wu B.
europepmc   +1 more source

Sensorless variable impedance control of robot joints with 3K gear reducers based on hybrid dynamics model and meta-policy optimization. [PDF]

Front Robot AI
Sun J, Gao X, Yan L.
europepmc   +1 more source

Hindsight Experience Replay Accelerates Proximal Policy Optimization

Hindsight experience replay (HER) accelerates off-policy reinforcement learning algorithms for environments that emit sparse rewards by modifying the goal of the episode post-hoc to be some state achieved during the episode. Because post-hoc modification
Crowder, Douglas C., McKenzie, Darrien M., Trappett, Matthew L., Chance, Frances S.   +3 more
core  

Development of novel reinforcement learning-based optimizer to impede tumor growth via radiochemotherapy. [PDF]

Sci Rep
Arsalan M, Yu X, Sadiq MT.
europepmc   +1 more source

Efficient Morphology-Control Co-Design via Stackelberg Proximal Policy Optimization [PDF]

Morphology-control co-design concerns the coupled optimization of an agent's body structure and control policy. This problem exhibits a bi-level structure, where the control dynamically adapts to the morphology to maximize performance.
Dai, Yanning, Schmidhuber, Juergen, Wang, Yuhui, Ashley, Dylan   +3 more
core   +1 more source

Dependency-Aware Task Offloading for Cloud-Edge Collaborative Computing in High-Speed Railways

Tongxin xuebao
To address the characteristics of complex perception tasks in high-speed railway scenarios, including directed acyclic graph (DAG) dependencies, high-speed mobility, and dynamically varying trackside computing resources, a mobility- and dependency-aware ...
HU Jingtian, CAO Yuan, SUN Yongkui, WANG Feng   +3 more
doaj  

Pediatric fracture classification in plain radiographs using EfficientNetV2 with proximal policy optimization fine-tuning. [PDF]

Sci Rep
Barhoush M, Khasawneh R, Alhatamleh S, Al Mamlook RE, Mistareehi DM, Nawashi H, Obeidat AH, Obeidat MA, Almuflih AS, Jaradat A, Alishwait A, Amin M.   +11 more
europepmc   +1 more source

Towards Autonomous Bio-Inspired Optimization: Deep Reinforcement Learning for Adaptive Metaheuristic Orchestration. [PDF]

Biomimetics (Basel)
Carrasco C, Crawford B, Valle C, Zulantay R, Giachetti G, Soto R.   +5 more
europepmc   +1 more source