The goal of this work is to evaluate the task of autonomous driving in urban environment using Deep Q-Network Agents. For this purpose, several approaches based ...
In this study, we propose an end-to-end autonomous driving system using a Deep Q-Network (DQN) and long-short-term memory (LSTM) with a new observation input.
Based on this research the paper introduces a deep reinforcement learning centered autonomous system for lane change maneuvers. The decision making and timing ...
DQN-based Reinforcement Learning for Vehicle Control of Autonomous ...
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In this paper, we propose a Novel Reinforcement Learning based model using Deep-Q Networks to control the AV in a complex scenario involving vehicles and ...
Nov 19, 2020 · The goal of this work is to evaluate the task of autonomous driving in urban environment using Deep Q-Network Agents.
“DQN-based Deep Reinforcement Learning for Autonomous Driving”, in Workshop of Physical Agents (WAF). Pérez-Gil, Oscar; Barea, Rafael; López-Guillén, Elena; ...
This paper presents an ethical decision-making model for self-driving cars in critical urban traffic situations, utilizing deep reinforcement learning (DQN ...
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“DQN-based Deep Reinforcement Learning for Autonomous Driving”, in Workshop of Physical Agents (WAF). Pérez-Gil, Oscar; Barea, Rafael; López-Guillén, Elena; ...
May 5, 2021 · This paper presents the implementation of DQN to an autonomous self-driving vehicle control in two different simulated environments.
Jan 13, 2022 · This paper proposes the using of algorithms based on Deep Learning (DL) in the control layer of an autonomous vehicle.