Abstract: |
Neuroevolution is a promising learning method in tasks with\n extremely large state and action spaces and hidden states. Recent\n advances allow neuroevolution to take place in real time, making it\n possible to e.g. construct video games with adaptive agents. Often\n some of the desired behaviors for such agents are known, and it\n would make sense to prescribe them, rather than requiring evolution\n to discover them. This paper presents a technique for incorporating\n human-generated advice in real time into neuroevolution. The advice\n is given in a formal language and converted to a neural network\n structure through KBANN. The NEAT neuroevolution method then\n incorporates the structure into existing networks through evolution\n of network weights and topology. The method is evaluated in the NERO\n video game, where it makes learning faster even when the tasks\n change and novel ways of making use of the advice are required. Such\n ability to incorporate human knowledge into neuroevolution in real\n time may prove useful in several interactive adaptive domains in the\n future. |
