Topographic Products of Experts applied to a Motocross Simulation and Simulation Stabilisation Benoit Chaperot, Colin Fyfe School of Computing The University of Paisley Paisley, PA1 2BE, SCOTLAND. [email protected][email protected]
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A topology preserving mapping, similar to Kohonen’s Self Organizing map. Points which are mapped close to one another share some common feature while points which are mapped far from one another do not share this feature.
is the number of inputs; the inputs are the state or situation of the bike, i.e. position, orientation and velocity of the bike relative to the track, and information about the terrain. In previous work, these were the inputs to supervised Artificial Neural Networks.
is the number of outputs; the outputs are the decisions made according to the state or situation of the bike, i.e. accelerate or brake, turn left or right, lean forward or backward.
Experiments with ToPoE proved that ToPoE performs better than the Kohonen SOM at controlling motorbikes in the motocross simulation. Both topology preserving mapping techniques are outperformed by MLP networks.
The game is a very good platform to experiment with AI and evolution techniques; however it requires a lot of human work to maintain and update in relation to other tools and libraries.