Combine and compare evolutionary robotics and reinforcement Learning as methods of designing autonomous robots

The purpose of this paper is to present a comparison between two methods of building adaptive controllers for robots. In spite of the wide range of techniques which are used for defining autonomous robot architectures, few attempts have been made in comparing their performance under similar circumstances. This comparison is particularly important in establishing benchmarks and in determining the best approach methods. The robotic tasks in our research concern mainly the convergence of behaviors like obstacle avoidance, hitting targets and shortest path finding using various methods of synthesizing control architectures' parameters. The first approach that has been used combines Neural Networks and Genetic Algorithms in a simple yet robust controller using an Evolutionary Robotics technique. The second one introduces a manner of using Reinforcement Learning with a Neural Network based architecture. The experiments take place in a simulated 3D environment, which was designed to allow the development, testing and comparison of various controllers in terms of advantages and disadvantages in order to establish a benchmark for autonomous robots.