Results 71 to 80 of about 7,404,725 (288)

Safe Reinforcement Learning for Buildings: Minimizing Energy Use While Maximizing Occupant Comfort

Energies
With buildings accounting for 40% of global energy consumption, heating, ventilation, and air conditioning (HVAC) systems represent the single largest opportunity for emissions reduction, consuming up to 60% of commercial building energy while ...
Mohammad Esmaeili, Sascha Hammes, Samuele Tosatto, David Geisler-Moroder, Philipp Zech   +4 more
doaj   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Policy Bifurcation in Safe Reinforcement Learning

CoRR
Safe reinforcement learning (RL) offers advanced solutions to constrained optimal control problems. Existing studies in safe RL implicitly assume continuity in policy functions, where policies map states to actions in a smooth, uninterrupted manner; however, our research finds that in some scenarios, the feasible policy should be discontinuous or multi-
Wenjun Zou, Yao Lyu, Jie Li 0042, Yujie Yang, Shengbo Eben Li, Jingliang Duan, Xianyuan Zhan, Jingjing Liu, Yaqin Zhang, Keqiang Li   +9 more
openaire   +3 more sources

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

Adaptive Cruise Control Based on Safe Deep Reinforcement Learning

Sensors
Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-
Rui Zhao, Kui Wang, Wenbo Che, Yun Li, Yuze Fan, Fei Gao   +5 more
doaj   +1 more source

Electrolyte Design for Fast‐Charging Lithium‐Based Batteries

Advanced Materials, EarlyView.
A decade of progress in fast‐charging electrolytes for lithium batteries is reviewed. Electrolyte design strategies spanning solvents, salts, additives, and advanced systems, such as localized high‐concentration electrolytes (LHCEs), are summarized. Advanced diagnostic tools for lithium plating and interphase chemistry are discussed, with perspectives ...
Chen Liu, Zehao Cui, Arumugam Manthiram
wiley   +1 more source

Constraints Driven Safe Reinforcement Learning for Autonomous Driving Decision-Making

IEEE Access
Although reinforcement learning (RL) methodologies exhibit potential in addressing decision-making and planning problems in autonomous driving, ensuring the safety of the vehicle under all circumstances remains a formidable challenge in practical ...
Fei Gao, Xiaodong Wang, Yuze Fan, Zhenhai Gao, Rui Zhao   +4 more
doaj   +1 more source

Polyphenol‐Inspired Materials for Agricultural Applications

Advanced Materials, EarlyView.
This review outlines the use of polyphenol‐inspired materials for sustainable agriculture, highlighting their molecular design, interfacial assembly, structure–property relationships, and prospects toward precision agriculture, climate resilience, ecosystem protection, and circular bioeconomy strategies.
Haofu Liu, Omid Mazaheri, Zhixing Lin, Tianzheng Wang, Jingqu Chen, Yuang Gu, Xia Liang, Deli Chen, Frank Caruso   +8 more
wiley   +1 more source

Safe Reinforcement Learning with Minimal Supervision

CoRR
Initially submitted to ICML ...
Alexander Quessy, Thomas Richardson, Sebastian East   +2 more
openaire   +3 more sources

Fuzzy and tile coding approximation techniques for coevolution in reinforcement learning [PDF]

, 2005
PhDThis thesis investigates reinforcement learning algorithms suitable for learning in large state space problems and coevolution. In order to learn in large state spaces, the state space must be collapsed to a computationally feasible size and then ...
Tokarchuk, Laurissa Nadia
core   +4 more sources