Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data

Adrian Remonda, Sarah Krebs, Eduardo Enrique Veas, Granit Luzhnica, Roman Kern

Publikation: KonferenzbeitragPaperForschungBegutachtung

Abstract

This paper explores the use of reinforcement learning (RL) models for autonomous racing. In contrast to passenger cars, where safety is the top priority, a racing car aims to minimize the lap-time. We frame the problem as a reinforcement learning task with a multidimensional input consisting of the vehicle telemetry, and a continuous action space. To find out which RL methods better solve the problem and whether the obtained models generalize to driving on unknown tracks, we put 10 variants of deep de-terministic policy gradient (DDPG) to race in two experiments: i) studying how RL methods learn to drive a racing car and ii) studying how the learning scenario influences the capability of the models to generalize. Our studies show that models trained with RL are not only able to drive faster than the baseline open source handcrafted bots but also generalize to unknown tracks.
Originalspracheenglisch
PublikationsstatusVeröffentlicht - 2019
VeranstaltungWorkshop on Scaling-Up Reinforcement Learning : SURL - Macau, China
Dauer: 10 Aug 201916 Aug 2019
http://surl.tirl.info/?p=program&y=2019

Workshop

WorkshopWorkshop on Scaling-Up Reinforcement Learning
LandChina
Zeitraum10/08/1916/08/19
Internetadresse

Fingerprint

Reinforcement learning
Telemetering
Railroad cars
Passenger cars
Experiments

Schlagwörter

    Dies zitieren

    Remonda, A., Krebs, S., Veas, E. E., Luzhnica, G., & Kern, R. (2019). Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data. Beitrag in Workshop on Scaling-Up Reinforcement Learning , China.

    Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data. / Remonda, Adrian; Krebs, Sarah; Veas, Eduardo Enrique; Luzhnica, Granit; Kern, Roman.

    2019. Beitrag in Workshop on Scaling-Up Reinforcement Learning , China.

    Publikation: KonferenzbeitragPaperForschungBegutachtung

    Remonda A, Krebs S, Veas EE, Luzhnica G, Kern R. Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data. 2019. Beitrag in Workshop on Scaling-Up Reinforcement Learning , China.
    Remonda, Adrian ; Krebs, Sarah ; Veas, Eduardo Enrique ; Luzhnica, Granit ; Kern, Roman. / Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data. Beitrag in Workshop on Scaling-Up Reinforcement Learning , China.
    @conference{fca9e8ec584442b3985fcca818923efc,
    title = "Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data",
    abstract = "This paper explores the use of reinforcement learning (RL) models for autonomous racing. In contrast to passenger cars, where safety is the top priority, a racing car aims to minimize the lap-time. We frame the problem as a reinforcement learning task with a multidimensional input consisting of the vehicle telemetry, and a continuous action space. To find out which RL methods better solve the problem and whether the obtained models generalize to driving on unknown tracks, we put 10 variants of deep de-terministic policy gradient (DDPG) to race in two experiments: i) studying how RL methods learn to drive a racing car and ii) studying how the learning scenario influences the capability of the models to generalize. Our studies show that models trained with RL are not only able to drive faster than the baseline open source handcrafted bots but also generalize to unknown tracks.",
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    AU - Veas, Eduardo Enrique

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    N2 - This paper explores the use of reinforcement learning (RL) models for autonomous racing. In contrast to passenger cars, where safety is the top priority, a racing car aims to minimize the lap-time. We frame the problem as a reinforcement learning task with a multidimensional input consisting of the vehicle telemetry, and a continuous action space. To find out which RL methods better solve the problem and whether the obtained models generalize to driving on unknown tracks, we put 10 variants of deep de-terministic policy gradient (DDPG) to race in two experiments: i) studying how RL methods learn to drive a racing car and ii) studying how the learning scenario influences the capability of the models to generalize. Our studies show that models trained with RL are not only able to drive faster than the baseline open source handcrafted bots but also generalize to unknown tracks.

    AB - This paper explores the use of reinforcement learning (RL) models for autonomous racing. In contrast to passenger cars, where safety is the top priority, a racing car aims to minimize the lap-time. We frame the problem as a reinforcement learning task with a multidimensional input consisting of the vehicle telemetry, and a continuous action space. To find out which RL methods better solve the problem and whether the obtained models generalize to driving on unknown tracks, we put 10 variants of deep de-terministic policy gradient (DDPG) to race in two experiments: i) studying how RL methods learn to drive a racing car and ii) studying how the learning scenario influences the capability of the models to generalize. Our studies show that models trained with RL are not only able to drive faster than the baseline open source handcrafted bots but also generalize to unknown tracks.

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