b.bib

% Encoding: UTF-8

@Article{Loiacono2013,
  author      = {Daniele Loiacono and Luigi Cardamone and Pier Luca Lanzi},
  title       = {Simulated Car Racing Championship: Competition Software Manual},
  journal     = {CoRR},
  year        = {2013},
  volume      = {abs/1304.1672},
  abstract    = {This manual describes the competition software for the Simulated Car Racing Championship, an international competition held at major conferences in the field of Evolutionary Computation and in the field of Computational Intelligence and Games. It provides an overview of the architecture, the instructions to install the software and to run the simple drivers provided in the package, the description of the sensors and the actuators.},
  date        = {2013-04-05},
  eprint      = {http://arxiv.org/abs/1304.1672v2},
  eprintclass = {cs.AI},
  eprinttype  = {arXiv},
  file        = {:http\://arxiv.org/pdf/1304.1672v2:PDF},
  keywords    = {cs.AI, cs.CE},
}

@Article{Wymann+2000,
  author  = {Wymann, Bernhard and Espi{\'e}, Eric and Guionneau, Christophe and Dimitrakakis, Christos and Coulom, R{\'e}mi and Sumner, Andrew},
  title   = {Torcs, the open racing car simulator},
  journal = {Software available at http://torcs. sourceforge. net},
  year    = {2000},
  volume  = {4},
  number  = {6},
}

@InProceedings{munoz2009controller,
  author       = {Munoz, Jorge and Gutierrez, German and Sanchis, Araceli},
  title        = {Controller for torcs created by imitation},
  booktitle    = {2009 IEEE Symposium on Computational Intelligence and Games},
  year         = {2009},
  pages        = {271--278},
  organization = {IEEE},
}

@Article{Sallab2017,
  author    = {Ahmad EL Sallab and Mohammed Abdou and Etienne Perot and Senthil Yogamani},
  title     = {Deep Reinforcement Learning framework for Autonomous Driving},
  journal   = {Electronic Imaging},
  year      = {2017},
  volume    = {2017},
  number    = {19},
  pages     = {70--76},
  month     = {1},
  doi       = {10.2352/issn.2470-1173.2017.19.avm-023},
  publisher = {Society for Imaging Science {\&} Technology},
}

@InProceedings{Loiacono2008,
  author    = {Daniele Loiacono and Julian Togelius and Pier Luca Lanzi and Leonard Kinnaird-Heether and Simon M. Lucas and Matt Simmerson and Diego Perez and Robert G. Reynolds and Yago Saez},
  title     = {The {WCCI} 2008 simulated car racing competition},
  booktitle = {2008 {IEEE} Symposium On Computational Intelligence and Games},
  year      = {2008},
  month     = {12},
  publisher = {{IEEE}},
  doi       = {10.1109/cig.2008.5035630},
}

@InProceedings{Cardamone2009,
  author    = {Luigi Cardamone and Daniele Loiacono and Pier Luca Lanzi},
  title     = {Evolving competitive car controllers for racing games with neuroevolution},
  booktitle = {Proceedings of the 11th Annual conference on Genetic and evolutionary computation - {GECCO} {\textquotesingle}09},
  year      = {2009},
  publisher = {{ACM} Press},
  doi       = {10.1145/1569901.1570060},
}

@Article{lillicrap2015continuous,
  author  = {Lillicrap, Timothy P and Hunt, Jonathan J and Pritzel, Alexander and Heess, Nicolas and Erez, Tom and Tassa, Yuval and Silver, David and Wierstra, Daan},
  title   = {Continuous control with deep reinforcement learning},
  journal = {arXiv preprint arXiv:1509.02971},
  year    = {2015},
}

@Article{watkins1992q,
  author    = {Watkins, Christopher JCH and Dayan, Peter},
  title     = {Q-learning},
  journal   = {Machine learning},
  year      = {1992},
  volume    = {8},
  number    = {3-4},
  pages     = {279--292},
  publisher = {Springer},
}

@PhdThesis{watkins1989learning,
  author = {Watkins, Christopher John Cornish Hellaby},
  title  = {Learning from delayed rewards},
  school = {King's College, Cambridge},
  year   = {1989},
}

@InProceedings{7568309,
  author    = {G. D'Angelo and S. Ferretti and V. Ghini},
  title     = {Simulation of the Internet of Things},
  booktitle = {2016 International Conference on High Performance Computing Simulation (HPCS)},
  year      = {2016},
  pages     = {1-8},
  month     = {7},
  doi       = {10.1109/HPCSim.2016.7568309},
  keywords  = {Internet of Things;digital simulation;multi-agent systems;smart cities;Internet of Things simulation;IoT simulation;PADS approach;agent-based adaptive parallel and distributed simulation;large-scale smart cities;massively populated IoT environment;multilevel simulation;real-time execution;scalability;smart territories simulation;Adaptation models;Computational modeling;Internet of things;Load modeling;Scalability;Sensors;Synchronization;Internet of Things;Parallel and Distributed Simulation;Simulation;Smart Cities;Wireless},
}

@online{siteTorcs,
  author       = {Bernhard {Wymann} Eric Espi{\'e} and Christophe Guionneau and Christos Dimitrakakis and R{\'e}mi Coulom and Andrew Sumner},
  title        = {{TORCS}, {T}he {O}pen {R}acing {C}ar {S}imulator},
  howpublished = {http://www.torcs.org},
  year         = {2014},
}

@online{yan2016DDPG,
  author       = {Yanpan Lau},
  title        = {Using Keras and Deep Deterministic Policy Gradient to play TORCS},
  howpublished = {https://yanpanlau.github.io/2016/10/11/Torcs-Keras.html},
  year         = {2016},
}

@Article{koutnik2013evolving,
  author = {Koutn{\'\i}k, Jan and Cuccu, Giuseppe and Schmidhuber, J{\"u}rgen and Gomez, Faustino},
  title  = {Evolving large-scale neural networks for vision-based torcs},
  year   = {2013},
}

@inproceedings{koutnik2014evolving,
  title={Evolving deep unsupervised convolutional networks for vision-based reinforcement learning},
  author={Koutn{\'\i}k, Jan and Schmidhuber, J{\"u}rgen and Gomez, Faustino},
  booktitle={Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation},
  pages={541--548},
  year={2014},
  organization={ACM}
}

@InProceedings{Nikulin2018,
  author    = {Vsevolod Nikulin and Albert Podusenko and Ivan Tanev and Katsunori Shimohara},
  title     = {Evolving the autosteering of a car featuring a realistically simulated steering response},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference on - {GECCO} {\textquotesingle}18},
  year      = {2018},
  publisher = {{ACM} Press},
  doi       = {10.1145/3205455.3205547},
}

@InProceedings{mnih2016asynchronous,
  author    = {Mnih, Volodymyr and Badia, Adria Puigdomenech and Mirza, Mehdi and Graves, Alex and Lillicrap, Timothy and Harley, Tim and Silver, David and Kavukcuoglu, Koray},
  title     = {Asynchronous methods for deep reinforcement learning},
  booktitle = {International conference on machine learning},
  year      = {2016},
  pages     = {1928--1937},
}

@InProceedings{Munoz2010,
  author    = {Jorge Munoz and German Gutierrez and Araceli Sanchis},
  title     = {A human-like {TORCS} controller for the Simulated Car Racing Championship},
  booktitle = {Proceedings of the 2010 {IEEE} Conference on Computational Intelligence and Games},
  year      = {2010},
  month     = {8},
  publisher = {{IEEE}},
  doi       = {10.1109/itw.2010.5593318},
}

@InProceedings{Butz2009,
  author    = {Martin V. Butz and Thies D. Lonneker},
  title     = {Optimized sensory-motor couplings plus strategy extensions for the {TORCS} car racing challenge},
  booktitle = {2009 {IEEE} Symposium on Computational Intelligence and Games},
  year      = {2009},
  month     = {sep},
  publisher = {{IEEE}},
  doi       = {10.1109/cig.2009.5286458},
}

@Misc{caldeira,
  author   = {Caldeira, Clara Marques},
  title    = {TORCS Training Interface : uma ferramenta auxiliar ao desenvolvimento de pilotos do TORCS},
  month    = dec,
  year     = {2013},
  note     = {Monografia (graduação)—Universidade de Brasília, Brasília, 2013.},
  abstract = {A ineficiente maneira como os pilotos são testados e desenvolvidos para jogo e simulador de corrida TORCS é um problema relevante por conta das limitações impostas sobre trabalhos de desenvolvimento de pilotos, i.e., algoritmos que determinam o comportamento dos carros não controlados por jogadores humanos. Porque este software tem um papel de plataforma para benchmark de diferentes abordagens de Inteligência Articial, é importante que se procure mitigar tal problema. Aqui desenvolveu-se a TORCS Training Interface, uma ferramenta que oferece automatizações para melhorar a eficiência das chamadas desimulações e retornar dados mais completos – ambos fatores importantes para as necessárias avaliações que têm como objetivo estimar habilidades de pilotos. Os resultados dos testes comparativos realizados indicam que ousoda ferramenta é uma alternativa viável às abordagens observadas na literatura, apresentando vantagens que podem torná-la a forma mais adequada para processos similares aos considerados neste trabalho.
_____________________________________________________________________ ABSTRACT: The inefficient manner in which drivers are tested and developed for the racing game and simulator TORCS is a relevant problem because of the limitations imposed over projects of development of drivers, i.e., algorith ms that determine the behavior of cars that are not controlled by human players. Because this software has a role of benchmark for different techniques of Articial Intelligence, it is important to work on mitigating this problem. The TORCS Training Interface was developed, a tool that offers automatizations in order to improve the efficiency of simulation calls and return more complete data-both of which are important for the necessary evaluations that have as a goal estimating the fitness of drivers. Results of the comparative tests performed indicate that the use of the tool is a viable alternative to the approaches seen in the literature, presentin g advantages that can make it the most fitting to processes that are similar to the ones considered here.},
  url      = {http://bdm.unb.br/handle/10483/6807},
}

@InProceedings{Chen2015,
  author    = {Chenyi Chen and Ari Seff and Alain Kornhauser and Jianxiong Xiao},
  title     = {{DeepDriving}: Learning Affordance for Direct Perception in Autonomous Driving},
  booktitle = {2015 {IEEE} International Conference on Computer Vision ({ICCV})},
  year      = {2015},
  month     = {12},
  publisher = {{IEEE}},
  doi       = {10.1109/iccv.2015.312},
}

@Article{1975a,
  author    = {Mihaly Csikszentmihalyi},
  title     = {Play and Intrinsic Rewards},
  journal   = {Journal of Humanistic Psychology},
  year      = {1975},
  volume    = {15},
  number    = {3},
  pages     = {41--63},
  month     = {7},
  doi       = {10.1177/002216787501500306},
  publisher = {{SAGE} Publications},
}


@book{schell2010arte,
  title={A Arte De Game Design: O Livro Original},
  author={Schell, J.},
  isbn={9788535241983},
  url={https://books.google.com.br/books?id=4spMYgEACAAJ},
  year={2010},
  publisher={Taylor \& Francis}
}

@online{bestcars,
  author = {Best Cars UOL},
  title = {Fórmula 1: cilindrada, admissão de ar e outros limites},
  year = 2016,
  url = {http://bestcars.uol.com.br/bc/mais/cons-tecnico/formula-1-cilindrada-admissao-de-ar-e-outros-limites/},
  urldate = {2019-05-03}
}

@article{autonomousCars,
    author = {Bimbraw, Keshav},
    year = {2015},
    month = {01},
    pages = {191-198},
    title = {Autonomous Cars: Past, Present and Future - A Review of the Developments in the Last Century, the Present Scenario and the Expected Future of Autonomous Vehicle Technology},
    volume = {1},
    journal = {ICINCO 2015 - 12th International Conference on Informatics in Control, Automation and Robotics, Proceedings},
    doi = {10.5220/0005540501910198}
}

@inproceedings{Tognetti:2010:ERP:1877826.1877830,
 author = {Tognetti, Simone and Garbarino, Maurizio and Bonanno, Andrea Tommaso and Matteucci, Matteo and Bonarini, Andrea},
 title = {Enjoyment Recognition from Physiological Data in a Car Racing Game},
 booktitle = {Proceedings of the 3rd International Workshop on Affective Interaction in Natural Environments},
 series = {AFFINE '10},
 year = {2010},
 isbn = {978-1-4503-0170-1},
 location = {Firenze, Italy},
 pages = {3--8},
 numpages = {6},
 url = {http://doi.acm.org/10.1145/1877826.1877830},
 doi = {10.1145/1877826.1877830},
 acmid = {1877830},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {SFS, artifact removals, cross validation, data normalization, enjoyment classification, feature selection, genetic algorithms, k-nn, pairwise preference, physiological signals, torcs},
}

@article{sallab2016end,
  title={End-to-end deep reinforcement learning for lane keeping assist},
  author={Ahmad El Sallab and Mohammed Abdou and Etienne Perot and Senthil Yogamani},
  journal={arXiv preprint arXiv:1612.04340},
  year={2016}
}

@article{mnih2015human,
  title={Human-level control through deep reinforcement learning},
  author={Volodymyr Mnih and Koray Kavukcuoglu and David Silver and Andrei A Rusu and Joel Veness and Marc G Bellemare and Alex Graves and Martin Riedmiller and Andreas K Fidjeland and Georg Ostrovski and others},
  journal={Nature},
  volume={518},
  number={7540},
  pages={529},
  year={2015},
  publisher={Nature Publishing Group}
}

@misc{ganesh2016deep,
  title={Deep Reinforcement Learning for Simulated Autonomous Driving},
  author={Ganesh, Adithya and Charalel, Joe and Sarma, Matthew Das and Xu, Nancy},
  year={2016},
  publisher={Stanford University}
}

@article{silver2018general,
  title={A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play},
  author={Silver, David and Hubert, Thomas and Schrittwieser, Julian and Antonoglou, Ioannis and Lai, Matthew and Guez, Arthur and Lanctot, Marc and Sifre, Laurent and Kumaran, Dharshan and Graepel, Thore and others},
  journal={Science},
  volume={362},
  number={6419},
  pages={1140--1144},
  year={2018},
  publisher={American Association for the Advancement of Science}
}

@online{timeArticle,
  author = {Time},
  title = {The Amazing Video Game Boom},
  year = 1993,
  url = {http://content.time.com/time/subscriber/article/0,33009,979289-1,00.html},
  urldate = {2019-05-24}
}

@online{newzoo2018,
  author = {Newzoo},
  title = {Mobile Revenues Account for More Than 50\% of the Global Games Market as It Reaches \$137.9 Billion in 2018},
  year = 2018,
  url = {https://newzoo.com/insights/articles/global-games-market-reaches-137-9-billion-in-2018-mobile-games-take-half/},
  urldate = {2019-05-24}
}

@online{semantix,
  author = {Semantix},
  title = {10 Algoritmos de Machine Learning que você precisa conhecer},
  year = 2017,
  url = {https://www.semantix.com.br/blog/10-algoritmos-de-machine-learning},
  urldate = {2019-05-27}
}


@online{medium,
  author = {Medium},
  title = {Aprendizagem de Maquina: Supervisionada ou Não Supervisionada?},
  year = 2016,
  url = {https://medium.com/opensanca/aprendizagem-de-maquina-supervisionada-ou-não-supervisionada-7d01f78cd80a},
  urldate = {2019-05-27}
}

@online{freecodecamp,
  author = {freeCodeCamp},
  title = {A brief introduction to reinforcement learning},
  year = 2018,
  url = {https://www.freecodecamp.org/news/a-brief-introduction-to-reinforcement-learning-7799af5840db/?gi=6df0f5fb0cdb},
  urldate = {2019-05-27}
}

@INPROCEEDINGS{5479101,
author={Fernando {Bevilacqua} and C. T. {Pozzer} and M. C. {d'Ornellas}},
booktitle={2009 VIII Brazilian Symposium on Games and Digital Entertainment},
title={Charack: Tool for Real-Time Generation of Pseudo-Infinite Virtual Worlds for 3D Games},
year={2009},
volume={},
number={},
pages={111-120},
keywords={computer games;virtual reality;Charack;pseudo-infinite virtual world;3D games;MMO game;terrain generation;content management method;Random number generation;Noise generators;Humans;Content management;Fractals;Hardware;Computer graphics;Usability;Continents;Surfaces;virtual worlds;terrain generation;3D games;noise;procedural generation;multifractal},
doi={10.1109/SBGAMES.2009.21},
ISSN={2159-6654},
month={Oct},
}

@article{bevilacquaferramenta,
  title={FERRAMENTA PARA GERA{\c{C}}{\~A}O DE MUNDOS VIRTUAIS PSEUDO-INFINITOS PARA JOGOS 3D MMO},
  author={Fernando Bevilacqua},
  year={2009},

}

@inproceedings{xia2016control,
  title={A control strategy of autonomous vehicles based on deep reinforcement learning},
  author={Xia, Wei and Li, Huiyun and Li, Baopu},
  booktitle={2016 9th International Symposium on Computational Intelligence and Design (ISCID)},
  volume={2},
  pages={198--201},
  year={2016},
  organization={IEEE}
}


@online{gpRestritora,
  author = {{Grande Prêmio}},
  title = {Depois de acidente assustador, Dillon pede velocidades mais baixas na Nascar: “Você só reza e espera chegar ao final”},
  year = 2015,
  url = {https://www.grandepremio.com.br/nascar/noticias/depois-de-acidente-assustador-dillon-pede-velocidades-mais-baixas-na-nascar-voce-so-reza-e-espera-chegar-ao-final},
  urldate = {2019-05-24}
}

@inproceedings{cardamone2011interactive,
  title={Interactive evolution for the procedural generation of tracks in a high-end racing game},
  author={Cardamone, Luigi and Loiacono, Daniele and Lanzi, Pier Luca},
  booktitle={Proceedings of the 13th annual conference on Genetic and evolutionary computation},
  pages={395--402},
  year={2011},
  organization={ACM}
}

@ARTICLE{5420018,
author={C. {Pedersen} and J. {Togelius} and G. N. {Yannakakis}},
journal={IEEE Transactions on Computational Intelligence and AI in Games},
title={Modeling Player Experience for Content Creation},
year={2010},
volume={2},
number={1},
pages={54-67},
keywords={computer games;content management;learning (artificial intelligence);multilayer perceptrons;optimisation;content creation;computational intelligence techniques;player experience quantitative models;gameplay metrics;platform game;Super Mario Bros game;player pairwise preference data;forced choice questionnaires;neuroevolutionary preference learning;multilayer perceptrons;design parameter optimization;data preference learning;Game theory;Computational intelligence;Artificial intelligence;Predictive models;Multilayer perceptrons;Design optimization;Mathematical model;Algorithm design and analysis;Technological innovation;Augmented reality;Content creation;fun;neuroevolution;platform games;player experience;player satisfaction modeling;preference learning},
doi={10.1109/TCIAIG.2010.2043950},
ISSN={1943-068X},
month={3},}

@inproceedings{togelius2007towards,
  title={Towards automatic personalised content creation for racing games},
  author={Togelius, Julian and De Nardi, Renzo and Lucas, Simon M},
  booktitle={2007 IEEE Symposium on Computational Intelligence and Games},
  pages={252--259},
  year={2007},
  organization={IEEE}
}

@ARTICLE{samuelML,
author={A. L. {Samuel}},
journal={IBM Journal of Research and Development},
title={Some Studies in Machine Learning Using the Game of Checkers},
year={1959},
volume={3},
number={3},
pages={210-229},
keywords={},
doi={10.1147/rd.33.0210},
ISSN={0018-8646},
month={7},}

@article{Sun2013,
  doi = {10.1007/s00521-013-1362-6},
  url = {https://doi.org/10.1007/s00521-013-1362-6},
  year = {2013},
  month = {2},
  publisher = {Springer Nature},
  volume = {23},
  number = {7-8},
  pages = {2031--2038},
  author = {Shiliang Sun},
  title = {A survey of multi-view machine learning},
  journal = {Neural Computing and Applications}
}

@article{stone2000multiagent,
  title={Multiagent systems: A survey from a machine learning perspective},
  author={Stone, Peter and Veloso, Manuela},
  journal={Autonomous Robots},
  volume={8},
  number={3},
  pages={345--383},
  year={2000},
  publisher={Springer}
}

@INPROCEEDINGS{7785853,
author={B. H. F. {Macedo} and G. F. P. {Araujo} and G. S. {Silva} and M. C. {Crestani} and Y. B. {Galli} and G. N. {Ramos}},
booktitle={2015 14th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)},
title={Evolving Finite-State Machines Controllers for the Simulated Car Racing Championship},
year={2015},
volume={},
number={},
pages={160-172},
keywords={computer games;control engineering computing;finite state machines;genetic algorithms;remotely operated vehicles;finite-state machines controllers;simulated car racing championship;autonomous vehicles;software controllers development;finite state-machine model;self-driving car;five-state driver;three-state drivers;The Open Racing Car Simulator;AUTOPIA controller;learning module;genetic algorithm;Automobiles;Genetic algorithms;Robot sensing systems;Thyristors;Games;Testing;finite-state machine;genetic algorithm;TORCS;simulated car racing},
doi={10.1109/SBGames.2015.19},
ISSN={2159-6662},
month={11},}

@ARTICLE{5482132,
author={Luigi {Cardamone} and Daniele {Loiacono} and Pier Luca {Lanzi}},
journal={IEEE Transactions on Computational Intelligence and AI in Games},
title={Learning to Drive in the Open Racing Car Simulator Using Online Neuroevolution},
year={2010},
volume={2},
number={3},
pages={176-190},
keywords={computer games;greedy algorithms;Internet;learning (artificial intelligence);online neuroevolution;nonplayer characters;the open racing car simulator;TORCS;online learning;Machine learning;Testing;Performance evaluation;Application software;Computational modeling;Stochastic processes;Computer simulation;Learning systems;Humans;Pattern matching;Machine learning;neuroevolution;online learning;online neuroevolution;simulated car racing},
doi={10.1109/TCIAIG.2010.2052102},
ISSN={1943-068X},
month={9},}

@inproceedings{cardamone2010searching,
  title={Searching for the optimal racing line using genetic algorithms},
  author={Luigi Cardamone and Daniele Loiacono and Pier Luca Lanzi and Alessandro Pietro Bardelli},
  booktitle={Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games},
  pages={388--394},
  year={2010},
  organization={IEEE}
}

@inproceedings{quadflieg2010learning,
  title={Learning the track and planning ahead in a car racing controller},
  author={Jan Quadflieg and Mike Preuss and Oliver Kramer and G{\"u}nter Rudolph},
  booktitle={Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games},
  pages={395--402},
  year={2010},
  organization={IEEE}
}

@article{justesen2019deep,
  title={Deep learning for video game playing},
  author={Niels Justesen and Philip Bontrager and Julian Togelius and Sebastian Risi},
  journal={IEEE Transactions on Games},
  year={2019},
  publisher={IEEE}
}

@INPROCEEDINGS{loiacono2010,
author={Daniele Loiacono and Luigi Cardamone and Pier Luca Lanzi and Alessandro Pietro Bardelli},
booktitle={IEEE Congress on Evolutionary Computation},
title={Learning to overtake in TORCS using simple reinforcement learning},
year={2010},
volume={},
number={},
pages={1-8},
keywords={computer games;learning (artificial intelligence);programming;public domain software;TORCS;reinforcement learning;programming;nonplayer characters;sophisticated behavior;computational intelligence;behavior-based architecture;The Open Racing Car Simulator;open source racing game;advanced braking policy;Q-learning;dynamically changing game situation;Driver circuits;Trajectory;Aerodynamics;Games;Sensors;Learning;Computational modeling},
doi={10.1109/CEC.2010.5586191},
ISSN={1089-778X},
month={7},}

@book{RLAnIntroduction,
  Author = {Richard S. Sutton and Andrew G. Barto},
  Title = {Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)},
  Publisher = {A Bradford Book},
  Year = {2015}
}

@INPROCEEDINGS{huang2015,
author={H. {Huang} and T. {Wang}},
booktitle={2015 IEEE Conference on Computational Intelligence and Games (CIG)},
title={Learning overtaking and blocking skills in simulated car racing},
year={2015},
volume={},
number={},
pages={439-445},
keywords={computer games;digital simulation;learning (artificial intelligence);traffic engineering computing;blocking skills;overtaking skills;Q-learning;simulated car racing games;complicated racing skills;driving AI agent;machine learning;opponent types;track characteristics;actual built-in tracks;TORCS;Games;Automobiles;Artificial intelligence;Niobium;Trajectory;Vehicle crash testing;Shape;Q-learning;TORCS;Car Racing},
doi={10.1109/CIG.2015.7317916},
ISSN={2325-4289},
month={8},}

@INPROCEEDINGS{patel2011,
author={P. G. {Patel} and N. {Carver} and S. {Rahimi}},
booktitle={2011 Federated Conference on Computer Science and Information Systems (FedCSIS)},
title={Tuning computer gaming agents using Q-learning},
year={2011},
volume={},
number={},
pages={581-588},
keywords={computer games;learning (artificial intelligence);software agents;computer gaming agent tuning;Q-learning;game AI;computer video games;game player;computer game bots;intelligent realistic AI agents;game map;game rules;game type;machine learning techniques;reinforcement learning technique;dynamic intelligent bots;online learning algorithm;Games;Weapons;Green products;Humans;Computers;Machine learning;Terrorism},
doi={},
ISSN={},
month={9},}

@article{Bellemare2012,
author = {G Bellemare, Marc and Veness, Joel and Bowling, Michael},
year = {2012},
month = {01},
pages = {},
title = {Investigating Contingency Awareness Using Atari 2600 Games},
volume = {2},
journal = {Proceedings of the National Conference on Artificial Intelligence}
}

@article{MnihKSGAWR13,
  author    = {Volodymyr Mnih and
               Koray Kavukcuoglu and
               David Silver and
               Alex Graves and
               Ioannis Antonoglou and
               Daan Wierstra and
               Martin A. Riedmiller},
  title     = {Playing Atari with Deep Reinforcement Learning},
  journal   = {CoRR},
  volume    = {abs/1312.5602},
  year      = {2013},
  url       = {http://arxiv.org/abs/1312.5602},
  archivePrefix = {arXiv},
  eprint    = {1312.5602},
  timestamp = {Mon, 13 Aug 2018 16:47:42 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/MnihKSGAWR13},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{kim2012generalization,
author = {Kim, Kyung-Joong and Seo, Jun-Ho and Park, Jung-Guk and Chae Na, Joong},
year = {2012},
month = {07},
pages = {87–99},
title = {Generalization of TORCS car racing controllers with artificial neural networks and linear regression analysis},
volume = {88},
journal = {Neurocomputing},
doi = {10.1016/j.neucom.2011.06.034}
}