In this paper , we describe a novel application of an existing Lagrangian dispersion modeling system to achieve real-time simulation and visualization of an urban plume that a user can interact with in a virtual environment ( VE ) through the utilization of commodity graphics hardware , utilizing the highly parallel computational capabilities available on graphics processing units ( GPU ) . GPUs have quickly developed from video game technology to open up new avenues for enhancing simulation performance and visualization of engineering and science applications . For computer graphics applications , GPUs provide highly parallel and inexpensive data paths for processing geometry and pixels , but for simulation these parallel paths are exploited for solving general problems . As the task became more difficult ; they were able to sustain better performance with more profound increase in prefrontal oxyenation ( p≪0.008 ) . The results imply that video game playing alters visual skills and hand-eye coordination . Higher prefrontal oxygenation observed in video game players suggests that limited vascular resources in their brain might be transferred towards areas involved in executive function . Perceived alertness was also increased by video game play . DISCUSSION : These results indicate that video game play could be an effective fatigue countermeasure in high vigilance occupations such as air traffic control . Fowler LA , Gustafson D. Video game play as a fatigue countermeasure in air traffic controllers . A platform perspective . The video game market has witnessed several significant structural changes . Our paper tries to explain the market structure dynamics of video game industry from the perspective of platform competition . The contemporary video game market is more and more crowded with titles that are expanded by digital contents that can be downloaded for free or payment : from annexing additional costumes to the characters , to implementing large portions of narrative , today expansion packs represent a phenomenon potentially and concretely capable of redefining the lifespan of each video game initially sold as a standalone creation , to then prove to be partial when new contents are released . This essay will try to group the variety of these digital contents into two essential sets , and then it will try to understand if the video games expanded by future downloadable contents can be included among the canons of serial gaming , now explicitly represented by the advent of episodic video games . Gauging Student Perceptions . Serious games , or video game-based technology applied to training , learning applications , provide a high fidelity simulation of particular environments and situations that focus on high level skills that are required in the field . Given the popularity of video games , particularly with today 's generation of learners , and the growing trend of restricted resident work hours and diminished operating room exposure due to limited budgets increased case complexity and medicolegal concerns , serious games provide a cost-effective viable training option . The results of the analysis indicate that a successful adaptation of a novel to the video game format requires extensive knowledge of story arc development as well as gameplay mechanics . The difference in text type between novel and video game creates many opportunities in which the audiovisual component , which is an integral part of the video game format , can be employed to replace the loss of narrative that is experienced . The genre definitions of each text dictate certain stylistic features that must be present in order to conform to the end users ’ expectations of the text . This paper proposes a concept of narrative as the design of an immersive simulation to be experienced by the interactor in a video game . We face this narrative status as the ongoing shift of a process faced with the nature of the video game format : in one side , the immersive nature of character-oriented games , and on the other , the simulative nature of god games and such . We believe the combination of these two features allows for the emergence of a new and promising narrative game format . One of the core courses in the undergraduate mechanical engineering curriculum has been completely redesigned . In the new numerical methods course , all assignments and learning experiences are built around a video/computer game . Students are given the task of writing computer programs to race a simulated car around a track . A Look at Student Engagement*. One of the core courses in the undergraduate mechanical engineering curriculum has been completely redesigned . In the new numerical methods course , all assignments and learning experiences are built around a video/computer game . Students are given the task of writing computer programs to race a simulated car around a track . We collected 160 questionnaires and theresults indicated that 61.9 % of the teachers used a stereo , 59 % used datashow , 49.6 % used computers , 40.3 % television andDVD , 25.2 % smartphones , 23.7 % camera Of photography / filming and 13.7 % overhead projector . We highlight that 5 % used thevideo game , which reveals the presence of new digital technologies also in public schools . The combination of all three aspects might overwhelm learners . To facilitate the deeper comprehension of NTS concepts , we describe an innovative video-based game , the Non-Technical Skills ( NTS ) Bingo . Participants get NTS Bingo cards that show five NTS elements each . We instead explore the use of almost effortlessly labeled virtual images for training high - quality segmentation networks . In , annotating a synthetic image took only 7 seconds on average through a computer game . For the urban scenes , we use the SYNTHIA dataset which contains images of a virtual city . Recently , hausknecht_12 proposed HyperNEAT - GGP , an evolutionary approach for finding policies to play Atari 2600 games . Although HyperNEAT - GGP is presented as a general game playing approach , it is currently difficult to assess its general performance as the reported results were limited to only two games . Finally , some of the authors of this paper recently presented a domain - independent feature generation technique that attempts to focus its effort around the location of the player avatar . subsection : Evaluation Frameworks for General Agents Although the idea of using games to evaluate the performance of agents has a long history in artificial intelligence , it is only more recently that an emphasis on generality has assumed a more prominent role . pell93strategy In this way the Atari 2600 platform provides AI researchers with something unique : clean , easily - emulated domains which nevertheless provide many of the challenges typically associated with real - world applications . Should technology advance so as to render general Atari 2600 game playing achievable , our challenge problem can always be extended to use more recent video game platforms . A natural progression , for example , would be to move on to the Commodore 64 , then to the Nintendo , and so forth towards current generation consoles . We show that our method outperforms the Consensus Kalman Filter in recovering the centralized estimate given a fixed communication bandwidth . We also demonstrate the algorithm in a high fidelity urban driving simulator ( CARLA ) , in which 50 autonomous cars connected on a time-varying communication network track the positions and velocities of 50 target vehicles using on-board cameras . If the NOHIMS CAI system architecture is to be applicable to a wide range of system applications , the alternative regarded as best is the MUMPS-based ABC system --- an Intelligent Tutoring System . ABC consists of three major executable components named ABLE , BILL , and CARLA . ABLE is used to specify the structures ( data , file , and procedure ) for the application , descriptive textual information needed for instruction , and curriculum structures . Furthermore , we release an evaluation benchmark for what we refer to as relocalization tracking . It has been created using the CARLA simulator as well as sequences taken from the Oxford RobotCar Dataset . In this research , we have employed global search and global optimization techniques based on simulated annealing ( SA ) and continuous action reinforcement learning automata ( CARLA ) for global training of hidden Markov models . The main goal is comparing CARLA method to other continuous global optimization methods like SA . Experimental results show that the CARLA outperforms SA . Recently , Rodriguez et al. [ 3 ] performed an analysis of the CARLA algorithm . The result of this analysis was an improvement of the CARLA method in terms of computation effort and local convergence properties . The improved automaton performs very well in single agent problems , but still has suboptimal performance with respect to global convergence in multi-agent settings . The CARLA algorithm has successfully been applied to control problems [ 2 , 1 ] . However in real world applications systems can be coupled and each subsystem is to be controlled by an individual controller . This method of controlling MTdc networks using RL techniques is called Online Distributed Voltage Control ( ODVC ) . The ODVC strategy uses Continuous Action Reinforcement Learning Automata ( CARLA ) to optimize power flows in real time . To validate the effectiveness of the proposed control method , dynamic simulations are carried out using a MTdc grid model composed of six nodes , interconnecting three offshore wind farms to three European countries . However , although minimumCORBA requires less memory than its " big brother " , further measures have to be taken to efficiently use the restricted resources on handheld devices . In this paper , we describe some results of our work on the CARLA architecture which are related to the question of reducing the memory footprint of CORBA-based mobile agents and their runtime environment for use on handheld devices . We present different approaches and discuss the advantages and disadvantages of each solution . In order to provide interoperability with the well-established CORBA world , our architecture is based on the minimumCORBA communication infrastructure , which is a reduced variant of the full CORBA specification . Reducing the memory footprint of CORBA-based mobile agents and their runtime environment for use on handheld devices is one of the most important design goals for the CARLA architecture and , therefore , is the main focus in this paper . Different approaches to this problem are presented and advantages as well as disadvantages of each solution are discussed . We train our policy with conditional imitation learning from realistic language data collected from human drivers and navigators . Through quantitative and interactive experiments within the CARLA framework , we show that our model can successfully interpret language instructions and follow them safely , even when generalizing to previously unseen environments . Code and video are available at this https URL . It is meaningful to compare the control performances of current main imitation learning methods based on the provided dataset . In this paper , we compare three typical imitation learning algorithms : Behavior cloning , Dataset Aggregation ( DAgger ) and Information maximizing Generative Adversarial Imitation Learning ( InfoGAIL ) in the The Open Racing Car Simulator ( TORCS ) and Car Learning to Act ( CARLA ) simulators , respectively . The performance of algorithms is evaluated on lane-keeping task in racing and urban environment . As a first step , in this work we investigate the possibility of semantically classifying different parts of a given scene in 3D by learning the underlying geometric context in addition to the texture cues BUT in the absence of labelled real-world datasets . To this end we generate a large number of synthetic scenes , their pixel-wise labels and corresponding 3D representations using CARLA software framework . We then build a deep neural network that learns underlying category specific 3D representation and texture cues from color information of the rendered synthetic scenes . The assumption that a location information would improve semantic segmentation performance emerges from the idea that some elements of urban scenes are located in a predictable manner . This assumption is confronted to realistic data on the CARLA autonomous driving simulator , which is used to create our own synthetic dataset with images , depth maps and bird-eye-view cartographic images . Simulators circumvent the difficulties due to the scarcity of publicly available synchronous labeled images and location information . For instance , VR enables researchers to analyze oculomotor behavior , which is fundamental in the field of car driving and is considered an indicator of attentional deployment . The work presented in this paper is based on a car driving simulator currently under development and aimed at studying the cognitive processes involved in car driving such as attentional processes and anticipatory mechanisms . However , recent work suggests that people can be too trusting of the technology with which they interact during emergencies , causing potential harm to themselves . To test whether this " over-trust " also extends to normal day-to-day activities , such as driving a car , we carried out a series of experiments with an autonomous car simulator . Participants ( N=73 ) engaged in a scenario with no , correct or false audible information regarding the state of traffic around the self-driving vehicle , and were told they could assume control at any point in the interaction . Recent work suggests that people can be too trusting of technology , leading to potential dangerous situations . We carried out a series of experiments in an autonomous car simulator , in order to test if there is a difference in people»s behavior when real-life consequences are applied , compared to pure simulation . The study was carried out with six experimental conditions in a between-subject design in which participants ( N = 121 ) interacted with the simulator and were told they could assume control of the autonomous car at any point during the simulation . The present study was undertaken to explore the feasibility of two new paradigms which replicate that interplay in virtual reality . We used car driving simulator software to implement a virtual car-driving task as well as a virtual street-crossing task . Either task was administered alone , as well as concurrently with a battery of loading tasks that mimicked activities of everyday life . Application of piezofilm movement sensors integrated into the car seat , seat belt and steering wheel was proposed for development of a non-invasive and hybrid systems for detecting driver drowsiness . A car simulator study was designed to collect physiological data for validation of this technology . Methodology for analysis of physiological data , independent assessment of driver drowsiness and development of drowsiness detection algorithm by means of sequential fitting and selection of regression models is presented . Car virtual driving simulator has widely applications in the fields of driver training , ergonomics research and automotive product measurement . It can simulate various space gestures during the running of the vehicle , such as bumps , crashes , turns , weightlessness and others . Therefore , this paper discussed about the comparisons between the proposed formula to quantify and classify the skill index of human operator with the Fuzzy Logic and other formula . An experiment using a driving car simulator is carried out to clarify those formulas in machine manipulation system . From the comparison it is found that the proposed formulas give better result compared to Fuzzy Logic System and other formula . For the discrete actions category , we will deal with Deep Q-Network Algorithm ( DQN ) while for the continuous actions category , we will deal with Deep Deterministic Actor Critic Algorithm ( DDAC ) . In addition to that , We will also discover the performance of these two categories on an open source car simulator for Racing called ( TORCS ) which stands for The Open Racing car Simulator . Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction with other vehicles . We demonstrate that such joint learning and supervised guidance facilitates hierarchical task decomposition , assisting the agent to learn faster , achieve better driving performance and increases transparency of the otherwise black-box end-to-end network . We run our experiments to validate the MT-LfD framework in CARLA , an open-source urban driving simulator . We introduce multiple non-player agents in CARLA and induce temporal noise in them for realistic stochasticity . We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains : a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies , each represented by deep neural networks5,6 . We evaluated our agent , AlphaStar , in the full game of StarCraft II , through a series of online games against human players . AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8 % of officially ranked human players . Other possible tools were also researched . An evaluation of AlphaStar software , as well as a description of its characteristics , can be found next in the report . The results obtained from the scripts written by the author are then presented , describing the convergence issues and the strategies followed to try to solve them .
In this paper , we describe a novel application of an existing Lagrangian dispersion modeling system to achieve real-time simulation and visualization of an urban plume that a user can interact with in a virtual environment ( VE ) through the utilization of commodity graphics hardware , utilizing the highly parallel computational capabilities available on graphics processing units ( GPU ) . GPUs have quickly developed from video game technology to open up new avenues for enhancing simulation performance and visualization of engineering and science applications . For computer graphics applications , GPUs provide highly parallel and inexpensive data paths for processing geometry and pixels , but for simulation these parallel paths are exploited for solving general problems .
As the task became more difficult ; they were able to sustain better performance with more profound increase in prefrontal oxyenation ( p≪0.008 ) . The results imply that video game playing alters visual skills and hand-eye coordination . Higher prefrontal oxygenation observed in video game players suggests that limited vascular resources in their brain might be transferred towards areas involved in executive function .
Perceived alertness was also increased by video game play . DISCUSSION : These results indicate that video game play could be an effective fatigue countermeasure in high vigilance occupations such as air traffic control . Fowler LA , Gustafson D. Video game play as a fatigue countermeasure in air traffic controllers .
A platform perspective . The video game market has witnessed several significant structural changes . Our paper tries to explain the market structure dynamics of video game industry from the perspective of platform competition .
The contemporary video game market is more and more crowded with titles that are expanded by digital contents that can be downloaded for free or payment : from annexing additional costumes to the characters , to implementing large portions of narrative , today expansion packs represent a phenomenon potentially and concretely capable of redefining the lifespan of each video game initially sold as a standalone creation , to then prove to be partial when new contents are released . This essay will try to group the variety of these digital contents into two essential sets , and then it will try to understand if the video games expanded by future downloadable contents can be included among the canons of serial gaming , now explicitly represented by the advent of episodic video games .
Gauging Student Perceptions . Serious games , or video game-based technology applied to training , learning applications , provide a high fidelity simulation of particular environments and situations that focus on high level skills that are required in the field . Given the popularity of video games , particularly with today 's generation of learners , and the growing trend of restricted resident work hours and diminished operating room exposure due to limited budgets increased case complexity and medicolegal concerns , serious games provide a cost-effective viable training option .
The results of the analysis indicate that a successful adaptation of a novel to the video game format requires extensive knowledge of story arc development as well as gameplay mechanics . The difference in text type between novel and video game creates many opportunities in which the audiovisual component , which is an integral part of the video game format , can be employed to replace the loss of narrative that is experienced . The genre definitions of each text dictate certain stylistic features that must be present in order to conform to the end users ’ expectations of the text .
This paper proposes a concept of narrative as the design of an immersive simulation to be experienced by the interactor in a video game . We face this narrative status as the ongoing shift of a process faced with the nature of the video game format : in one side , the immersive nature of character-oriented games , and on the other , the simulative nature of god games and such . We believe the combination of these two features allows for the emergence of a new and promising narrative game format .
One of the core courses in the undergraduate mechanical engineering curriculum has been completely redesigned . In the new numerical methods course , all assignments and learning experiences are built around a video/computer game . Students are given the task of writing computer programs to race a simulated car around a track .
A Look at Student Engagement*. One of the core courses in the undergraduate mechanical engineering curriculum has been completely redesigned . In the new numerical methods course , all assignments and learning experiences are built around a video/computer game . Students are given the task of writing computer programs to race a simulated car around a track .
We collected 160 questionnaires and theresults indicated that 61.9 % of the teachers used a stereo , 59 % used datashow , 49.6 % used computers , 40.3 % television andDVD , 25.2 % smartphones , 23.7 % camera Of photography / filming and 13.7 % overhead projector . We highlight that 5 % used thevideo game , which reveals the presence of new digital technologies also in public schools .
The combination of all three aspects might overwhelm learners . To facilitate the deeper comprehension of NTS concepts , we describe an innovative video-based game , the Non-Technical Skills ( NTS ) Bingo . Participants get NTS Bingo cards that show five NTS elements each .
We instead explore the use of almost effortlessly labeled virtual images for training high - quality segmentation networks . In , annotating a synthetic image took only 7 seconds on average through a computer game . For the urban scenes , we use the SYNTHIA dataset which contains images of a virtual city .
Recently , hausknecht_12 proposed HyperNEAT - GGP , an evolutionary approach for finding policies to play Atari 2600 games . Although HyperNEAT - GGP is presented as a general game playing approach , it is currently difficult to assess its general performance as the reported results were limited to only two games . Finally , some of the authors of this paper recently presented a domain - independent feature generation technique that attempts to focus its effort around the location of the player avatar .
subsection : Evaluation Frameworks for General Agents Although the idea of using games to evaluate the performance of agents has a long history in artificial intelligence , it is only more recently that an emphasis on generality has assumed a more prominent role . pell93strategy
In this way the Atari 2600 platform provides AI researchers with something unique : clean , easily - emulated domains which nevertheless provide many of the challenges typically associated with real - world applications . Should technology advance so as to render general Atari 2600 game playing achievable , our challenge problem can always be extended to use more recent video game platforms . A natural progression , for example , would be to move on to the Commodore 64 , then to the Nintendo , and so forth towards current generation consoles .
We show that our method outperforms the Consensus Kalman Filter in recovering the centralized estimate given a fixed communication bandwidth . We also demonstrate the algorithm in a high fidelity urban driving simulator ( CARLA ) , in which 50 autonomous cars connected on a time-varying communication network track the positions and velocities of 50 target vehicles using on-board cameras .
If the NOHIMS CAI system architecture is to be applicable to a wide range of system applications , the alternative regarded as best is the MUMPS-based ABC system --- an Intelligent Tutoring System . ABC consists of three major executable components named ABLE , BILL , and CARLA . ABLE is used to specify the structures ( data , file , and procedure ) for the application , descriptive textual information needed for instruction , and curriculum structures .
Furthermore , we release an evaluation benchmark for what we refer to as relocalization tracking . It has been created using the CARLA simulator as well as sequences taken from the Oxford RobotCar Dataset .
In this research , we have employed global search and global optimization techniques based on simulated annealing ( SA ) and continuous action reinforcement learning automata ( CARLA ) for global training of hidden Markov models . The main goal is comparing CARLA method to other continuous global optimization methods like SA . Experimental results show that the CARLA outperforms SA .
Recently , Rodriguez et al. [ 3 ] performed an analysis of the CARLA algorithm . The result of this analysis was an improvement of the CARLA method in terms of computation effort and local convergence properties . The improved automaton performs very well in single agent problems , but still has suboptimal performance with respect to global convergence in multi-agent settings . The CARLA algorithm has successfully been applied to control problems [ 2 , 1 ] . However in real world applications systems can be coupled and each subsystem is to be controlled by an individual controller .
This method of controlling MTdc networks using RL techniques is called Online Distributed Voltage Control ( ODVC ) . The ODVC strategy uses Continuous Action Reinforcement Learning Automata ( CARLA ) to optimize power flows in real time . To validate the effectiveness of the proposed control method , dynamic simulations are carried out using a MTdc grid model composed of six nodes , interconnecting three offshore wind farms to three European countries .
However , although minimumCORBA requires less memory than its " big brother " , further measures have to be taken to efficiently use the restricted resources on handheld devices . In this paper , we describe some results of our work on the CARLA architecture which are related to the question of reducing the memory footprint of CORBA-based mobile agents and their runtime environment for use on handheld devices . We present different approaches and discuss the advantages and disadvantages of each solution .
In order to provide interoperability with the well-established CORBA world , our architecture is based on the minimumCORBA communication infrastructure , which is a reduced variant of the full CORBA specification . Reducing the memory footprint of CORBA-based mobile agents and their runtime environment for use on handheld devices is one of the most important design goals for the CARLA architecture and , therefore , is the main focus in this paper . Different approaches to this problem are presented and advantages as well as disadvantages of each solution are discussed .
We train our policy with conditional imitation learning from realistic language data collected from human drivers and navigators . Through quantitative and interactive experiments within the CARLA framework , we show that our model can successfully interpret language instructions and follow them safely , even when generalizing to previously unseen environments . Code and video are available at this https URL .
It is meaningful to compare the control performances of current main imitation learning methods based on the provided dataset . In this paper , we compare three typical imitation learning algorithms : Behavior cloning , Dataset Aggregation ( DAgger ) and Information maximizing Generative Adversarial Imitation Learning ( InfoGAIL ) in the The Open Racing Car Simulator ( TORCS ) and Car Learning to Act ( CARLA ) simulators , respectively . The performance of algorithms is evaluated on lane-keeping task in racing and urban environment .
As a first step , in this work we investigate the possibility of semantically classifying different parts of a given scene in 3D by learning the underlying geometric context in addition to the texture cues BUT in the absence of labelled real-world datasets . To this end we generate a large number of synthetic scenes , their pixel-wise labels and corresponding 3D representations using CARLA software framework . We then build a deep neural network that learns underlying category specific 3D representation and texture cues from color information of the rendered synthetic scenes .
The assumption that a location information would improve semantic segmentation performance emerges from the idea that some elements of urban scenes are located in a predictable manner . This assumption is confronted to realistic data on the CARLA autonomous driving simulator , which is used to create our own synthetic dataset with images , depth maps and bird-eye-view cartographic images . Simulators circumvent the difficulties due to the scarcity of publicly available synchronous labeled images and location information .
For instance , VR enables researchers to analyze oculomotor behavior , which is fundamental in the field of car driving and is considered an indicator of attentional deployment . The work presented in this paper is based on a car driving simulator currently under development and aimed at studying the cognitive processes involved in car driving such as attentional processes and anticipatory mechanisms .
However , recent work suggests that people can be too trusting of the technology with which they interact during emergencies , causing potential harm to themselves . To test whether this " over-trust " also extends to normal day-to-day activities , such as driving a car , we carried out a series of experiments with an autonomous car simulator . Participants ( N=73 ) engaged in a scenario with no , correct or false audible information regarding the state of traffic around the self-driving vehicle , and were told they could assume control at any point in the interaction .
Recent work suggests that people can be too trusting of technology , leading to potential dangerous situations . We carried out a series of experiments in an autonomous car simulator , in order to test if there is a difference in people»s behavior when real-life consequences are applied , compared to pure simulation . The study was carried out with six experimental conditions in a between-subject design in which participants ( N = 121 ) interacted with the simulator and were told they could assume control of the autonomous car at any point during the simulation .
The present study was undertaken to explore the feasibility of two new paradigms which replicate that interplay in virtual reality . We used car driving simulator software to implement a virtual car-driving task as well as a virtual street-crossing task . Either task was administered alone , as well as concurrently with a battery of loading tasks that mimicked activities of everyday life .
Application of piezofilm movement sensors integrated into the car seat , seat belt and steering wheel was proposed for development of a non-invasive and hybrid systems for detecting driver drowsiness . A car simulator study was designed to collect physiological data for validation of this technology . Methodology for analysis of physiological data , independent assessment of driver drowsiness and development of drowsiness detection algorithm by means of sequential fitting and selection of regression models is presented .
Car virtual driving simulator has widely applications in the fields of driver training , ergonomics research and automotive product measurement . It can simulate various space gestures during the running of the vehicle , such as bumps , crashes , turns , weightlessness and others .
Therefore , this paper discussed about the comparisons between the proposed formula to quantify and classify the skill index of human operator with the Fuzzy Logic and other formula . An experiment using a driving car simulator is carried out to clarify those formulas in machine manipulation system . From the comparison it is found that the proposed formulas give better result compared to Fuzzy Logic System and other formula .
For the discrete actions category , we will deal with Deep Q-Network Algorithm ( DQN ) while for the continuous actions category , we will deal with Deep Deterministic Actor Critic Algorithm ( DDAC ) . In addition to that , We will also discover the performance of these two categories on an open source car simulator for Racing called ( TORCS ) which stands for The Open Racing car Simulator . Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction with other vehicles .
We demonstrate that such joint learning and supervised guidance facilitates hierarchical task decomposition , assisting the agent to learn faster , achieve better driving performance and increases transparency of the otherwise black-box end-to-end network . We run our experiments to validate the MT-LfD framework in CARLA , an open-source urban driving simulator . We introduce multiple non-player agents in CARLA and induce temporal noise in them for realistic stochasticity .
We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains : a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies , each represented by deep neural networks5,6 . We evaluated our agent , AlphaStar , in the full game of StarCraft II , through a series of online games against human players . AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8 % of officially ranked human players .
Other possible tools were also researched . An evaluation of AlphaStar software , as well as a description of its characteristics , can be found next in the report . The results obtained from the scripts written by the author are then presented , describing the convergence issues and the strategies followed to try to solve them .
Accelerating urban fast response Lagrangian dispersion simulations using inexpensive graphics processor parallelism Investigation of the effect of playing video games on visual attention and brain hemodynamics with functional near infrared spectroscopy Video Game Play as a Fatigue Countermeasure in Air Traffic Controllers. The market structure of the video game industry: A platform perspective Fragmentation: between expansion packs and episodic video games Serious Games in the Classroom: Gauging Student Perceptions An analysis of the strategies used in the adaptation of the novel 'Harry Potter and the philosopher's stone' into a video game format for the Playstation console using Skopos theory The design of narrative as an immersive simulation Effectiveness of using a video game to teach a course in mechanical engineering Video Game-Based Education in Mechanical Engineering: A Look at Student Engagement* TECHNOLOGIES AND THEIR USE BY PHYSICAL EDUCATION TEACHERS IN RIO DE JANEIRO Non-Technical Skills Bingo—a game to facilitate the learning of complex concepts Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract) Distributed Multi-Target Tracking for Autonomous Vehicle Fleets Design of a Computer-Aided Instructional Module for the Navy Occupational Health Information Management System (NOHIMS). Phase I. GN-Net: The Gauss-Newton Loss for Deep Direct SLAM Applying continuous action reinforcement learning automata(CARLA) to global training of hidden Markov models An RL approach to common-interest continuous action games Online distributed voltage control of an offshore MTdc network using reinforcement learning CARLA - a CORBA-based ARchitecture for Lightweight Agents Design considerations for a CORBA-based architecture for lightweight agents (CARLA) Conditional Driving from Natural Language Instructions Comparison of Control Methods Based on Imitation Learning for Autonomous Driving Multi Modal Semantic Segmentation using Synthetic Data Improving semantic segmentation in urban scenes with a cartographic information Using Virtual Reality Techniques to Study Cognitive Processes in Car Driving Activity Do We Blindly Trust Self-Driving Cars Simulations and Self-Driving Cars: A Study of Trust and Consequences An experimental paradigm for the assessment of realistic human multitasking Methodology and initial analysis results for development of non-invasive and hybrid driver drowsiness detection systems Road Parameter Calculation Algorithm Based on Derivation Comparisons of skill index algorithm in driving for human adaptive mechatronics End-to-End Deep Reinforcement Learning for Lane Keeping Assist Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision Grandmaster level in StarCraft II using multi-agent reinforcement learning Simulation of a 3D printing process
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DiGRA Conference MMVR DiGRA Conference 2017 IEEE International Conference on Computer Vision (ICCV) IJCAI ITCC (2) CEC IAT ICARCV VISIGRAPP (2: HUCAPP) HRI (Companion) HRI (Companion) ASCC
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