72 research outputs found
Neuroevolution in Games: State of the Art and Open Challenges
This paper surveys research on applying neuroevolution (NE) to games. In
neuroevolution, artificial neural networks are trained through evolutionary
algorithms, taking inspiration from the way biological brains evolved. We
analyse the application of NE in games along five different axes, which are the
role NE is chosen to play in a game, the different types of neural networks
used, the way these networks are evolved, how the fitness is determined and
what type of input the network receives. The article also highlights important
open research challenges in the field.Comment: - Added more references - Corrected typos - Added an overview table
(Table 1
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
Controller for TORCS created by imitation
Proceeding of: IEEE Symposium on Computational Intelligence and Games, 2009. CIG 2009, september 7-10, 2009, Milano, ItalyThis paper is an initial approach to create a controller for the game TORCS by learning how another controller or humans play the game. We used data obtained from two controllers and from one human player. The first controller is the winner of the WCCI 2008 Simulated Car Racing Competition, and the second one is a hand coded controller that performs a complete lap in all tracks. First, each kind of controller is imitated separately, then a mix of the data is used to create new controllers. The imitation is performed by means of training a feed forward neural network with the data, using the backpropagation algorithm for learning.This work was supported in part by the University Carlos III of Madrid under grant PIF UC3M01-0809 and by the Ministry of Science and Innovation under project TRA2007- 67374-C02-02
Gene regulated car driving: using a gene regulatory network to drive a virtual car
This paper presents a virtual racing car controller based on an artificial gene regulatory network. Usually used to control virtual cells in developmental models, recent works showed that gene regulatory networks are also capable to control various kinds of agents such as foraging agents, pole cart, swarm robots, etc. This paper details how a gene regulatory network is evolved to drive on any track through a three-stages incremental evolution. To do so, the inputs and outputs of the network are directly mapped to the car sensors and actuators. To make this controller a competitive racer, we have distorted its inputs online to make it drive faster and to avoid opponents. Another interesting property emerges from this approach: the regulatory network is naturally resistant to noise. To evaluate this approach, we participated in the 2013 simulated racing car competition against eight other evolutionary and scripted approaches. After its first participation, this approach finished in third place in the competition
A human-like TORCS controller for the Simulated Car Racing Championship
Proceeding of: IEEE Congres on Computational Intelligence and Games (CIG'10), Copenhagen (Denmark), 18-21, August, 2010.This paper presents a controller for the 2010 Simulated Car Racing Championship. The idea is not to create the fastest controller but a human-like controller. In order to achieve this, first we have created a process to build a model of the tracks while the car is running and then we used several neural networks which predict the trajectory the car should follow and the target speed. A scripted policy is used for the gear change and to follow the predicted trajectory with the predicted speed. The neural networks are trained with data retrieved from a human player, and are evaluated in a new track. The results shows an acceptable performance of the controller in unknown tracks, more than 20% slower than the human in the same tracks because of the mistakes made when the controller tries to follow the trajectory.This work was supported in part by the University Carlos III of Madrid under grant PIF UC3M01-0809 and by the Ministry of Science and Innovation under project TRA2007-
67374-C02-02
Evolution of Neural Networks for Helicopter Control: Why Modularity Matters
The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so
Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution
Although automated test generation is common in many programming domains,
games still challenge test generators due to their heavy randomisation and
hard-to-reach program states. Neuroevolution combined with search-based
software testing principles has been shown to be a promising approach for
testing games, but the co-evolutionary search for optimal network topologies
and weights involves unreasonably long search durations. In this paper, we aim
to improve the evolutionary search for game input generators by integrating
knowledge about human gameplay behaviour. To this end, we propose a novel way
of systematically recording human gameplay traces, and integrating these traces
into the evolutionary search for networks using traditional gradient descent as
a mutation operator. Experiments conducted on eight diverse Scratch games
demonstrate that the proposed approach reduces the required search time from
five hours down to only 52 minutes
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