Transfer of learning for complex task domains: a demonstration using multiple robots

This paper demonstrates a learning mechanism for complex tasks. Such tasks may be inherently expensive to learn in terms of training time and/or cost of obtaining each training pattern. Learning simple, safe tasks and extending them to more complex tasks can cause faster convergence to the solution. This method has been formalized and demonstrated on a simulated multiple robot (multi-robot) scenario. The objective is to effectively search out and destroy stationary hostile agents present in an unknown urban terrain map. Using the presented method, the robots learn how to effectively map the area, and then improve their learning modules for the complex task. The robots are simple behavioral agents with minimal communication