Results 111 to 120 of about 1,052,376 (334)
物联网学报, 2023 Deep reinforcement learning has strong abilities of decision-making and generalization and often applies to the quality of service (QoS) optimization in software defined network (SDN).However, traditional deep reinforcement learning algorithms have ...
Cenhuishan LIAO +4 more
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Similarity-Based Hyperspectral Band Selection Using Deep Reinforcement Learning
, 2022 The main goal of hyperspectral band selection is to select a subset of bands to reduce the redundancy in hyperspectral images. Deep reinforcement learning was recently introduced for this task, which adopts a deep Q-network as the agent and information ...
Tuxworth, Gervase, Zhou, Jun, Bao, Dong
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Learn to Steer through Deep Reinforcement Learning [PDF]
Sensors, 2018 It is crucial for robots to autonomously steer in complex environments safely without colliding with any obstacles. Compared to conventional methods, deep reinforcement learning-based methods are able to learn from past experiences automatically and enhance the generalization capability to cope with unseen circumstances. Therefore, we propose an end-to-
Keyu Wu 0002 +3 more
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Advanced Engineering Materials, EarlyView.A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source
Deep Reinforcement Learning Approach for Traffic Light Control and Transit Priority
Future TransportationThis study investigates the use of deep reinforcement learning techniques to improve traffic signal control systems through the integration of deep learning and reinforcement learning approaches.
Saeed Mansouryar +3 more
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Understanding representation learning for deep reinforcement learning
Representation learning is essential to practical success of reinforcement learning. Through a state representation, an agent can describe its environment to efficiently explore the state space, generalize to new states and perform credit assignment from
Le Lan, Charline
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Deep Successor Reinforcement Learning
CoRR, 2016 Learning robust value functions given raw observations and rewards is now possible with model-free and model-based deep reinforcement learning algorithms. There is a third alternative, called Successor Representations (SR), which decomposes the value function into two components -- a reward predictor and a successor map.
Tejas D. Kulkarni +3 more
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Advanced Engineering Materials, EarlyView.Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley +1 more source
Learning Mobile Manipulation through Deep Reinforcement Learning
Sensors, 2020 Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator.
Cong Wang +7 more
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Quantum Deep Recurrent Reinforcement Learning
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 Recent advances in quantum computing (QC) and machine learning (ML) have drawn significant attention to the development of quantum machine learning (QML). Reinforcement learning (RL) is one of the ML paradigms which can be used to solve complex sequential decision making problems.
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