Evolving robust and specialized car racing skills

Neural network-based controllers are evolved for racing simulated R/C cars around several tracks of varying difficulty. The transferability of driving skills acquired when evolving for a single track is evaluated, and different ways of evolving controllers able to perform well on many different tracks are investigated. It is further shown that such generally proficient controllers can reliably be developed into specialized controllers for individual tracks. Evolution of sensor parameters together with network weights is shown to lead to higher final fitness, but only if turned on after a general controller is developed, otherwise it hinders evolution. It is argued that simulated car racing is a scalable and relevant testbed for evolutionary robotics research, and that the results of this research can be useful for commercial computer games

Application of serious games to sport, health and exercise

Use of interactive entertainment has been exponentially expanded since the last decade. Throughout this 10+ year evolution there has been a concern about turning entertainment properties into serious applications, a.k.a "Serious Games". In this article we present two set of Serious Game applications, an Environment Visualising game which focuses solely on applying serious games to elite Olympic sport and another set of serious games that incorporate an in house developed proprietary input system that can detect most of the human movements which focuses on applying serious games to health and exercise

AI Researchers, Video Games Are Your Friends!

If you are an artificial intelligence researcher, you should look to video games as ideal testbeds for the work you do. If you are a video game developer, you should look to AI for the technology that makes completely new types of games possible. This chapter lays out the case for both of these propositions. It asks the question "what can video games do for AI", and discusses how in particular general video game playing is the ideal testbed for artificial general intelligence research. It then asks the question "what can AI do for video games", and lays out a vision for what video games might look like if we had significantly more advanced AI at our disposal. The chapter is based on my keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad audience.Comment: in Studies in Computational Intelligence Studies in Computational Intelligence, Volume 669 2017. Springe

Deep learning for video game playing

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

Weymouth's once in a lifetime opportunity.

On the 6 July 2005 much changed for the towns of Weymouth and Portland, Dorset as they heard that in seven years time they would be hosting the sailing for the successful London 2012 Olympic Bid. Two years later on, and whilst the Weymouth and Portland National Sailing Academy (WPNSA) has swung into action with its preparations, the town of Weymouth itself in some respects appears to be unsure of it’s future direction. At the time of the bid the road issue was still uppermost in the minds of the residents; however that issue has potentially been resolved with the Government announcing the decision to build the relief road in time for the 2012 Games which will alter significantly the arrival of visitors into the town from Dorchester, until now a potential traffic nightmare for both visitors and residents alike with long delays and traffic bottlenecks. Yet, within the town itself, little has changed. Many plans are being suggested about developments including the new Pavilion Peninsula and also the redevelopment of the waterfront esplanade, but it would appear that the town is slightly indecisive as to where it wants to position itself with regard to attracting visitors and income to the town. Weymouth needs to decide on its strategy for the future, by adopting a concerted approach to attract certain segments of the market and invest sensibly in these areas, rather than trying to spread itself too thinly across all market sectors. The role of a good events portfolio could be a major contributor to a successful marketing strategy. From research undertaken in 2004 ( Sadd, 2004 ), it is evident that the locals are keen for the ‘ season ’ to be all year around and they recognise the importance of events in the town and how, historically, they have been a great source of celebration, ranging from the military parades to the hosting of the tall ships for an unprecedented three times. This chapter will explain the historical underpinning of Weymouth’s present situation and will make suggestions for possible events based strategies to maximise the potential benefits to be gained from being Olympic Hosts in 2012

Learning to Race through Coordinate Descent Bayesian Optimisation

In the automation of many kinds of processes, the observable outcome can often be described as the combined effect of an entire sequence of actions, or controls, applied throughout its execution. In these cases, strategies to optimise control policies for individual stages of the process might not be applicable, and instead the whole policy might have to be optimised at once. On the other hand, the cost to evaluate the policy's performance might also be high, being desirable that a solution can be found with as few interactions as possible with the real system. We consider the problem of optimising control policies to allow a robot to complete a given race track within a minimum amount of time. We assume that the robot has no prior information about the track or its own dynamical model, just an initial valid driving example. Localisation is only applied to monitor the robot and to provide an indication of its position along the track's centre axis. We propose a method for finding a policy that minimises the time per lap while keeping the vehicle on the track using a Bayesian optimisation (BO) approach over a reproducing kernel Hilbert space. We apply an algorithm to search more efficiently over high-dimensional policy-parameter spaces with BO, by iterating over each dimension individually, in a sequential coordinate descent-like scheme. Experiments demonstrate the performance of the algorithm against other methods in a simulated car racing environment.Comment: Accepted as conference paper for the 2018 IEEE International Conference on Robotics and Automation (ICRA

Arms races and car races

Evolutionary car racing (ECR) is extended to the case of two cars racing on the same track. A sensor representation is devised, and various methods of evolving car controllers for competitive racing are explored. ECR can be combined with co-evolution in a wide variety of ways, and one aspect which is explored here is the relative-absolute fitness continuum. Systematical behavioural differences are found along this continuum; further, a tendency to specialization and the reactive nature of the controller architecture are found to limit evolutionary progress