Results 141 to 150 of about 1,436,832 (344)

Reinforcement Learning and Video Games [PDF]

arXiv, 2019
Reinforcement learning has exceeded human-level performance in game playing AI with deep learning methods according to the experiments from DeepMind on Go and Atari games. Deep learning solves high dimension input problems which stop the development of reinforcement for many years.
arxiv  

Iron‐MOFs for Biomedical Applications

Advanced Healthcare Materials, Volume 14, Issue 8, March 25, 2025.
This review introduces the unique properties of iron‐based metal–organic frameworks (Fe‐MOFs), the advances in synthesis and formulation, and their state‐of‐the‐art applications in biomedicine. It discusses the challenges in transitioning Fe‐MOFs from research to clinical use, emphasizing the need for eco‐friendly production methods, and a deeper ...
Zhihao Yu, Mathilde Lepoitevin, Christian Serre   +2 more
wiley   +1 more source

One‐Shot Remote Integration of Macromolecular Synaptic Elements on a Chip for Ultrathin Flexible Neural Network System

Advanced Materials, EarlyView.
A novel one‐shot integration electropolymerization (OSIEP) method is developed as a breakthrough on the intricate photolithographic steps, enabling to compress all processes from synthesis to channel integration in one‐shot manufacturing. The specially designed dual bipolar electrodes provide the targeted depositions of poly(3,4‐ethylenedioxythiophene)
Jiyun Lee, Jaehoon Lee, Hyeonsu Bang, Tae Woong Yoon, Jong Hwan Ko, Guobing Zhang, Ji‐Sang Park, Il Jeon, Sungjoo Lee, Boseok Kang   +9 more
wiley   +1 more source

Classifying Options for Deep Reinforcement Learning

, 2016
In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the policy network, and a supervisory network for choosing between the ...
Arulkumaran, Kai, Bharath, Anil Anthony, Dilokthanakul, Nat, Shanahan, Murray   +3 more
core  

Deep auto-encoder neural networks in reinforcement learning [PDF]

, 2010
Sascha Lange, Martin Riedmiller
openalex   +1 more source

Learning to Walk Via Deep Reinforcement Learning

Robotics: Science and Systems XV, 2019
RSS 2019, https://sites.google.com/view/minitaur-locomotion/
Haarnoja, Tuomas, Ha, Sehoon, Zhou, Aurick, Tan, Jie, Tucker, George, Levine, Sergey   +5 more
openaire   +2 more sources

Pushing Radiative Cooling Technology to Real Applications

Advanced Materials, EarlyView.
Radiative cooling controls surface optical properties for solar and thermal radiation, offering solutions for global warming and energy savings. Despite significant advances, key challenges remain: optimizing optical efficiency, maintaining aesthetics, preventing overcooling, enhancing durability, and enabling scalable production.
Chongjia Lin, Keqiao Li, Meng Li, Benjamin Dopphoopha, Jiongzhi Zheng, Jiazheng Wang, Shanshan Du, Yang Li, Baoling Huang   +8 more
wiley   +1 more source

A new approach for drone tracking with drone using Proximal Policy Optimization based distributed deep reinforcement learning

SoftwareX, 2023
In this paper, a distributed deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed for an unmanned aerial vehicle (UAV) to autonomously track another UAV. Accordingly, this paper makes three important contributions
Ziya Tan, Mehmet Karaköse
doaj  

Ultrahigh Specific Strength by Bayesian Optimization of Carbon Nanolattices

Advanced Materials, Volume 37, Issue 14, April 9, 2025.
Multi‐objective Bayesian optimization algorithm designs high‐strength nanolattices made of pyrolytic carbon which offer the compressive strength of carbon steel at the density of Styrofoam. This study combines machine learning with nanoscale strengthening and nanoscale 3D printing to achieve unattained material properties with millimeter scalability ...
Peter Serles, Jinwook Yeo, Michel Haché, Pedro Guerra Demingos, Jonathan Kong, Pascal Kiefer, Somayajulu Dhulipala, Boran Kumral, Katherine Jia, Shuo Yang, Tianjie Feng, Charles Jia, Pulickel M. Ajayan, Carlos M. Portela, Martin Wegener, Jane Howe, Chandra Veer Singh, Yu Zou, Seunghwa Ryu, Tobin Filleter   +19 more
wiley   +1 more source

A Survey Analyzing Generalization in Deep Reinforcement Learning [PDF]

arXiv
Reinforcement learning research obtained significant success and attention with the utilization of deep neural networks to solve problems in high dimensional state or action spaces. While deep reinforcement learning policies are currently being deployed in many different fields from medical applications to large language models, there are still ongoing
arxiv