Results 51 to 60 of about 1,629,210 (315)

Comparison of On-Policy Deep Reinforcement Learning A2C with Off-Policy DQN in Irrigation Optimization: A Case Study at a Site in Portugal

Computers, 2022
Precision irrigation and optimization of water use have become essential factors in agriculture because water is critical for crop growth. The proper management of an irrigation system should enable the farmer to use water efficiently to increase ...
Khadijeh Alibabaei, Pedro D. Gaspar, Eduardo Assunção, Saeid Alirezazadeh, Tânia M. Lima, Vasco N. G. J. Soares, João M. L. P. Caldeira   +6 more
doaj   +1 more source

Multi-Objective Secure Task Offloading Strategy for Blockchain-Enabled IoV-MEC Systems: A Double Deep Q-Network Approach

IEEE Access
The Internet of Vehicles (IoV) represents a paradigm shift in vehicular communication, aiming to enhance traffic efficiency, safety, and the driving experience by leveraging interconnected vehicles.
Komeil Moghaddasi, Shakiba Rajabi, F. S. Gharehchopogh   +2 more
semanticscholar   +1 more source

Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

, 2018
Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair.
AK McCallum, D Dancey, E Ikonomovska, M Hall, M Riedmiller, N Landwehr, P Chaudhuri, RS Sutton, S Tong, V Mnih, WY Loh   +10 more
core   +1 more source

Three-Stage Bidding Strategy of Generation Company Based on Double Deep Q-Network under Incomplete Information Condition

Zhongguo dianli, 2021
In power market with incomplete information, a generation company only knows its own relevant information, while biddings of other market members and market environment may affect the market clearing result, which impacts the generation company’s revenue,
Pengpeng YANG, Beibei WANG, Peng XU, Gaoqin WANG, Yaxian ZHENG   +4 more
doaj   +1 more source

A Deep Hierarchical Approach to Lifelong Learning in Minecraft

, 2016
We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledge-base.
Givony, Shahar, Mankowitz, Daniel J., Mannor, Shie, Tessler, Chen, Zahavy, Tom   +4 more
core   +1 more source

Implementing the Deep Q-Network

, 2017
The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and building point for much deep reinforcement learning research. However, replicating results for complex systems is often challenging since original scientific publications are not always able to describe in detail every important parameter setting and software engineering ...
Roderick, Melrose, MacGlashan, James, Tellex, Stefanie   +2 more
openaire   +2 more sources

Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning

, 2018
This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a ...
Hoel, Carl-Johan, Laine, Leo, Wolff, Krister   +2 more
core   +1 more source

Study of Q-learning and deep Q-network learning control for a rotary inverted pendulum system

Discover Applied Sciences
The rotary inverted pendulum system (RIPS) is an underactuated mechanical system with highly nonlinear dynamics and it is difficult to control a RIPS using the classic control models.
Zied Ben Hazem
semanticscholar   +1 more source

DELP Treatment on Vision and Retinal Microcirculation in Patients With Acute Ischemic Stroke: Report of Five Cases and Literature Review

Therapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Background The delipid extracorporeal lipoprotein filter from plasma (DELP) treatment can effectively reduce blood lipid, increase blood flow, and improve neurological deficits in patients with acute ischemic stroke (AIS). However, its effect on vision and retinal microcirculation in stroke patients has never been reported.
Ning Li, Ronghua Hong, Hua You, Feiping Xu, Qingjun Zhang, Yiwen Lin, Yuting Su, Danmei Lan, Lingjing Jin, Junhui She   +9 more
wiley   +1 more source

BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems

, 2017
We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems. Our agents explore via Thompson sampling, drawing Monte Carlo samples from a Bayes-by-Backprop neural network.
Ahmed, Faisal, Deng, Li, Gao, Jianfeng, Li, Lihong, Li, Xiujun, Lipton, Zachary C.   +5 more
core   +2 more sources