Comparative Study of Q-Learning and NeuroEvolution of Augmenting Topologies for Self Driving Agents
Autonomous driving vehicles have been of keen interest ever since automation of various tasks started. Humans are prone to exhaustion and have a slow response time on the road, and on top of that driving is already quite a dangerous task with around 1.35 million road traffic incident deaths each yea...
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| creator | Ishtiaq, Arhum Anees, Maheen Mahmood, Sara Jafry, Neha |
| description | Autonomous driving vehicles have been of keen interest ever since automation
of various tasks started. Humans are prone to exhaustion and have a slow
response time on the road, and on top of that driving is already quite a
dangerous task with around 1.35 million road traffic incident deaths each year.
It is expected that autonomous driving can reduce the number of driving
accidents around the world which is why this problem has been of keen interest
for researchers. Currently, self-driving vehicles use different algorithms for
various sub-problems in making the vehicle autonomous. We will focus
reinforcement learning algorithms, more specifically Q-learning algorithms and
NeuroEvolution of Augment Topologies (NEAT), a combination of evolutionary
algorithms and artificial neural networks, to train a model agent to learn how
to drive on a given path. This paper will focus on drawing a comparison between
the two aforementioned algorithms. |
| doi_str_mv | 10.48550/arxiv.2209.09007 |
| format | Article |
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of various tasks started. Humans are prone to exhaustion and have a slow
response time on the road, and on top of that driving is already quite a
dangerous task with around 1.35 million road traffic incident deaths each year.
It is expected that autonomous driving can reduce the number of driving
accidents around the world which is why this problem has been of keen interest
for researchers. Currently, self-driving vehicles use different algorithms for
various sub-problems in making the vehicle autonomous. We will focus
reinforcement learning algorithms, more specifically Q-learning algorithms and
NeuroEvolution of Augment Topologies (NEAT), a combination of evolutionary
algorithms and artificial neural networks, to train a model agent to learn how
to drive on a given path. This paper will focus on drawing a comparison between
the two aforementioned algorithms.</description><identifier>DOI: 10.48550/arxiv.2209.09007</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Learning ; Computer Science - Neural and Evolutionary Computing ; Computer Science - Robotics</subject><creationdate>2022-09</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2209.09007$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2209.09007$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Ishtiaq, Arhum</creatorcontrib><creatorcontrib>Anees, Maheen</creatorcontrib><creatorcontrib>Mahmood, Sara</creatorcontrib><creatorcontrib>Jafry, Neha</creatorcontrib><title>Comparative Study of Q-Learning and NeuroEvolution of Augmenting Topologies for Self Driving Agents</title><description>Autonomous driving vehicles have been of keen interest ever since automation
of various tasks started. Humans are prone to exhaustion and have a slow
response time on the road, and on top of that driving is already quite a
dangerous task with around 1.35 million road traffic incident deaths each year.
It is expected that autonomous driving can reduce the number of driving
accidents around the world which is why this problem has been of keen interest
for researchers. Currently, self-driving vehicles use different algorithms for
various sub-problems in making the vehicle autonomous. We will focus
reinforcement learning algorithms, more specifically Q-learning algorithms and
NeuroEvolution of Augment Topologies (NEAT), a combination of evolutionary
algorithms and artificial neural networks, to train a model agent to learn how
to drive on a given path. This paper will focus on drawing a comparison between
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of various tasks started. Humans are prone to exhaustion and have a slow
response time on the road, and on top of that driving is already quite a
dangerous task with around 1.35 million road traffic incident deaths each year.
It is expected that autonomous driving can reduce the number of driving
accidents around the world which is why this problem has been of keen interest
for researchers. Currently, self-driving vehicles use different algorithms for
various sub-problems in making the vehicle autonomous. We will focus
reinforcement learning algorithms, more specifically Q-learning algorithms and
NeuroEvolution of Augment Topologies (NEAT), a combination of evolutionary
algorithms and artificial neural networks, to train a model agent to learn how
to drive on a given path. This paper will focus on drawing a comparison between
the two aforementioned algorithms.</abstract><doi>10.48550/arxiv.2209.09007</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Learning Computer Science - Neural and Evolutionary Computing Computer Science - Robotics |
| title | Comparative Study of Q-Learning and NeuroEvolution of Augmenting Topologies for Self Driving Agents |
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