Transfer of Learning Across Compositions of Sequentail Tasks

Most \weak" learning algorithms, including reinforcement learning methods, have been applied on tasks with single goals. The e ort to build more sophisticated learning systems that operate in complex environments will require the ability to handle multiple goals. Methods that allow transfer of learning will play a crucial role in learning systems that support multiple goals. In this paper I describe a class of multiple tasks that represents a subset of routine animal activity. I present a new learning algorithm and an architecture that allows transfer of learning by the \sharing" of solutions to the common parts of multiple tasks. A proof of the algorithm is also provided.