Safe deep reinforcement learning

Results 51 to 60 of about 9,252,589 (289)

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang +3 more
wiley +1 more source

Vehicle Lateral Control Based on Augmented Lagrangian DDPG Algorithm

Applied Sciences
This paper studies the safe trajectory tracking control of intelligent vehicles, which is still an open and challenging problem. A deep reinforcement learning algorithm based on augmented Lagrangian safety constraints is proposed to the lateral control ...
Zhi Li, Meng Wang, Haitao Zhao
doaj +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj +8 more
wiley +1 more source

UAV swarm safe coverage path planning with deep reinforcement learning

Discover Computing
Unmanned aerial vehicles (UAVs) have been extensively deployed in various emergency rescue missions due to their cost-effectiveness, high maneuverability, and flexible flying capabilities. However, challenges such as densely packed towering buildings, no-
Lei Gao +4 more
doaj +1 more source

An Autonomous Vehicle Behavior Decision Method Based on Deep Reinforcement Learning with Hybrid State Space and Driving Risk

Sensors
Behavioral decision-making is an important part of the high-level intelligent driving system of intelligent vehicles, and efficient and safe behavioral decision-making plays an important role in the deployment of intelligent transportation system, which ...
Xu Wang, Bo Qian, Junchao Zhuo, Weiqun Liu +3 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 +3 more
wiley +1 more source

Research on Data-Driven Optimal Scheduling of Power System

Energies, 2023
The uncertainty of output makes it difficult to effectively solve the economic security dispatching problem of the power grid when a high proportion of renewable energy generating units are integrated into the power grid.
Jianxun Luo +4 more
doaj +1 more source

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

A Multi-Objective Collaborative Optimization Approach for Building Integrated Energy Systems Based on Deep Reinforcement Learning

Applied Sciences
To address the challenges of coordinated optimization in building integrated energy systems (IES) under the dual-carbon targets—characterized by strong multi-energy coupling, significant uncertainty in renewable generation, and stringent safety ...
Limin Wang +4 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

deep reinforcement learning
reinforcement learning
autonomous driving

path planning
adaptive cruise control
safe reinforcement learning

constrained markov decision process