Combining Multiple Inputs in HyperNEAT Mobile Agent Controller
2009
In this paper we present neuro-evolution of neural network controllers for mobile agents in a simulated environment. The controller is obtained through evolution of hypercube encoded weights of recurrent neural networks (HyperNEAT). The simulated agent's goal is to find a target in a shortest time interval. The generated neural network processes three different inputs --- surface quality, obstacles and distance to the target. A behavior emerged in agents features ability of driving on roads, obstacle avoidance and provides an efficient way of the target search.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
17
References
3
Citations
NaN
KQI