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Physics in medicine and biology
Nov 11, 2022
Reinforcement learning takes sequential decision-making approaches by learning the policy through trial and error based on interaction with the environment. Combining deep learning and reinforcement learning can empower the agent to learn the interac...
Sensors (Basel, Switzerland)
Nov 6, 2022
The rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warnin...
IEEE transactions on neural networks and learning systems
Oct 27, 2022
Auxiliary rewards are widely used in complex reinforcement learning tasks. However, previous work can hardly avoid the interference of auxiliary rewards on pursuing the main rewards, which leads to the destruction of the optimal policy. Thus, it is c...
Sensors (Basel, Switzerland)
Oct 16, 2022
Space situational awareness (SSA) is becoming increasingly challenging with the proliferation of resident space objects (RSOs), ranging from CubeSats to mega-constellations. Sensors within the United States Space Surveillance Network are tasked to re...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
This article develops an adaptive observation-based efficient reinforcement learning (RL) approach for systems with uncertain drift dynamics. A novel concurrent learning adaptive extended observer (CL-AEO) is first designed to jointly estimate the sy...
Computational intelligence and neuroscience
Sep 28, 2022
Despite the emergence of various human-robot collaboration frameworks, most are not sufficiently flexible to adapt to users with different habits. In this article, a Multimodal Reinforcement Learning Human-Robot Collaboration (MRLC) framework is prop...
Proceedings of the National Academy of Sciences of the United States of America
Sep 19, 2022
Regardless of how much data artificial intelligence agents have available, agents will inevitably encounter previously unseen situations in real-world deployments. Reacting to novel situations by acquiring new information from other people-socially s...
Computational intelligence and neuroscience
Sep 5, 2022
Advances in deep learning significantly affect reinforcement learning, which results in the emergence of Deep RL (DRL). DRL does not need a data set and has the potential beyond the performance of human experts, resulting in significant developments ...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
Efficient neural architecture search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing and reinforcement learning (RL). In the phase of architecture search, ENAS employs deep scalable architecture ...
Computational intelligence and neuroscience
Aug 31, 2022
For the problems of unreasonable computation offloading and uneven resource allocation in Mobile Edge Computing (MEC), this paper proposes a task offloading and resource allocation strategy based on deep learning for MEC. Firstly, in the multiuser mu...