Results 81 to 90 of about 7,404,725 (288)

Artificial Intelligence Meets Micro/Nanorobotics

Advanced Materials, EarlyView.
Artificial intelligence is transforming micro‐ and nanorobots from externally controlled, task‐specific machines into adaptive, autonomous systems. Machine learning, multimodal perception, digital twins, AI‐guided materials and geometry design enhance propulsion, localization, decision‐making, whichaccelerates clinical and environmental applications ...
Fatma M. Yurtsever, Minitha Cherukutty Ramakrishnan, Ian Kuula Ross, Ivan Zelinka, Hamed Shahsavan, Michal Otyepka, Martin Pumera   +6 more
wiley   +1 more source

Bioengineered Interfaces for Peripheral Nerve Sensory Restoration

Advanced Materials, EarlyView.
Half of amputees abandon their prosthetics for lack of feeling. This review charts the full path from peripheral nerve injury to restored sensation, through surgical, regenerative, noninvasive, and implanted approaches, and shows how injury type and interface material properties determine which strategy can deliver naturalistic feedback, and why ...
Sydney Swedick, Salim El Hadwe, Kesi Liang, George G. Malliaras, Damiano G. Barone   +4 more
wiley   +1 more source

Probabilistic Guarantees for Safe Deep Reinforcement Learning [PDF]

, 2020
Deep reinforcement learning has been successfully applied to many control tasks, but the application of such agents in safety-critical scenarios has been limited due to safety concerns. Rigorous testing of these controllers is challenging, particularly when they operate in probabilistic environments due to, for example, hardware faults or noisy sensors.
Edoardo Bacci, David Parker 0001
openaire   +4 more sources

Voltage Control Based on Multi-Agent Safe Deep Reinforcement Learning

Zhongguo dianli
To address issues of voltage limit violations and fluctuations caused by the high penetration of distributed photovoltaic (PV) systems in the distribution network, a voltage control method based on multi-agent safe deep reinforcement learning is proposed.
Yi ZENG, Yi ZHOU, Jixiang LU, Liangcai ZHOU, Ningkai TANG, Hong LI   +5 more
doaj   +1 more source

Descriptors to Dynamics: A Materials and Device Perspective on in‐Materio Physical Reservoir Computing for Neuromorphic Edge Intelligence

Advanced Materials, EarlyView.
Intrinsic material dynamics are harnessed as computational resources for neuromorphic in‐materio physical reservoir computing. Defects, ionic motion, interfaces, percolation, geometry, and biasing shape transient states that provide fading memory, nonlinearity, and high‐dimensional projection for simple readout. A descriptor‐to‐dynamics framework links
Kshitij RB Singh, Alif Syafiq Kamarol Zaman, Muzhen Xu, Yuki Usami, Saptarshi Das, Hirofumi Tanaka   +5 more
wiley   +1 more source

Probabilistic Shielding for Safe Reinforcement Learning

Proceedings of the AAAI Conference on Artificial Intelligence
In real-life scenarios, a Reinforcement Learning (RL) agent aiming to maximize their reward, must often also behave in a safe manner, including at training time. Thus, much attention in recent years has been given to Safe RL, where an agent aims to learn an optimal policy among all policies that satisfy a given safety constraint. However, strict safety
Edwin Hamel-De le Court, Francesco Belardinelli, Alexander W. Goodall   +2 more
openaire   +3 more sources

Radiative Thermal Management for Extreme Environments: Mechanisms and Design Strategies

Advanced Materials, EarlyView.
In this review, we discuss the fundamental mechanisms of radiative cooling and solar heating, material and structural design strategies, hybrid approaches integrating additional heat transfer pathways, and durability‐oriented designs addressing environmental stresses.
Hyung Rae Kim, Haeseong Kim, Joo Hwan Ko, Gil Ju Lee, Dae‐Hyeong Kim, Young Min Song   +5 more
wiley   +1 more source

Learning Strict Nash Equilibria through Reinforcement [PDF]

This paper studies the analytical properties of the reinforcement learning model proposed in Erev and Roth (1998), also termed cumulative reinforcement learning in Laslier et al (2001).
Ianni, Antonella
core  

Real-World Reinforcement Learning

, 2021
This is our first public release of real-world reinforcement learning, a document with associated code showing how to design and deploy reinforcement learning solutions in customer-facing applications.If you use this software, please cite it as ...
Mason, Douglas
core   +1 more source

Data-Driven Synthesis of Probabilistic Controlled Invariant Sets for Linear Markov Decision Processes

IEEE Open Journal of Control Systems
We study data-driven computation of probabilistic controlled invariant sets (PCIS) for safety-critical reinforcement learning under unknown dynamics. Assuming a linear MDP model, we use regularized least squares and self-normalized confidence bounds to ...
Kazumune Hashimoto, Shunki Kimura, Junya Ikemoto, Kazunobu Serizawa, Yulong Gao, Kai Cai   +5 more
doaj   +1 more source