In this paper, we propose new method, which can provide user the path to the target place efficiently. It stores the state of roads to target place as the ...
Abstract - In this paper, we propose new method which can provide user the path to the targetplace eficiently. It stores the state of roads to target place ...
Jul 14, 2023 · Our study introduces a significant novelty by proposing an optimization model that employs deep reinforcement learning (DRL) to manage traffic in mass transit ...
May 2, 2020 · 1) Deep Q-Network: Since value-based RL algorithms learn the Q-function by populating a Q-table, it is not feasible to visit all the states and ...
The aim of this paper is to develop insight into the potential of reinforcement learning (RL) agents and distributed reinforcement learning agents in the domain ...
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In this paper, an intersection-based routing method using Q-learning (IRQ) is presented for VANETs. IRQ uses both global and local views in the routing process.
Feb 2, 2023 · In this paper, we propose a deep reinforcement Q-learning model to optimize traffic signal control at an isolated intersection, in a partially observable ...
Sep 11, 2024 · In 2020, Joo et al. [ 60] presented a Q-learning-based Traffic Signal Control (TSC) system to maximize the number of vehicles crossing the ...
The aim of this study is to explore a reinforcement learning based adaptive optimal control model for traffic signals in intelligent transportation systems.
Feb 4, 2023 · Deep reinforcement learning (DRL) is a promising approach to adaptively control traffic lights based on the real-time traffic situation in a road network.