1992/06/01 by Randall D. Beer, John C. Gallagher · 3 citations
Computer Science · Neuroscience · #Neural Networks and Applications #Neural dynamics and brain function #Reinforcement Learning in Robotics
paper · doi:10.1177/105971239200100105
openalex publication_date 1992/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
We would like the behavior of the artificial agents that we construct to be as well-adapted to their environments as natural animals are to theirs. Unfortunately, designing controllers with these properties is a very difficult task. In this article, we demonstrate that continuous-time recurrent neural networks are a viable mechanism for adaptive agent control and that the genetic algorithm can be used to evolve effective neural controllers. A significant advantage of this approach is that one need specify only a measure of an agent's overall performance rather than the precise motor output trajectories by which it is achieved. By manipulating the performance evaluation, one can place selective pressure on the development of controllers with desired properties. Several novel controllers have been evolved, including a chemotaxis controller that switches between different strategies depending on environmental conditions, and a locomotion controller that takes advantage of sensory feedback if available but that can operate in its absence if necessary.