Exploring the T-Maze: Evolving Learning-Like Robot Behaviors using CTRNNs
This paper explores the capabilities of continuous time recurrent neural networks (CTRNNs) to display reinforcement learning-like abilities on a set of T-Maze and double T-Maze navigation tasks, where the robot has to locate and "remember'' the position of a reward-zone. The "learning'' comes about without modifications of synapse strengths, but simply from internal network dynamics, as proposed by . Neural controllers are evolved in simulation and in the simple case evaluated on a real robot. The evolved controllers are analyzed and the results obtained are discussed.