Results 121 to 130 of about 685,298 (326)
Practical Deep Reinforcement Learning Approach for Stock Trading
, 2018 Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy
Liu, Xiao-Yang +4 more
core
Generalization and Regularization in DQN [PDF]
arXiv, 2018 Deep reinforcement learning algorithms have shown an impressive ability to learn complex control policies in high-dimensional tasks. However, despite the ever-increasing performance on popular benchmarks, policies learned by deep reinforcement learning algorithms can struggle to generalize when evaluated in remarkably similar environments.
arxiv
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 +9 more
wiley +1 more source
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
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 +8 more
wiley +1 more source
Computational Modeling of Reticular Materials: The Past, the Present, and the Future
Advanced Materials, EarlyView.Reticular materials are advanced materials with applications in emerging technologies. A thorough understanding of material properties at operating conditions is critical to accelerate the deployment at an industrial scale. Herein, the status of computational modeling of reticular materials is reviewed, supplemented with topical examples highlighting ...
Wim Temmerman +3 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 +3 more
core
A Bio‐Inspired Perspective on Materials Sustainability
Advanced Materials, EarlyView.This perspective discusses natural materials as inspiration for sustainable engineering designs and the processing of materials. First, circularity, longevity, parsimony, and activity are presented as essential material paradigms. The perspective then uses many examples of natural and technical materials to introduce principles such as oligo ...
Wolfgang Wagermaier +2 more
wiley +1 more source
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 +5 more
openaire +2 more sources