Compact Q-Learning for Micro-robots
Scaling down robots to miniature size introduces many new challenges including memory and program size limitations, low processor performance and low power autonomy. In this paper we describe the concept and implementation of learning of safe-wandering and light following tasks on the autonomous micro-robots, Alice. We propose a simplified reinforcement learning algorithm based on one step Q-learning that is optimized in speed and memory consumption. This algorithm uses only integer-based sum operators and avoids floating-point and multiplication operators.