Permutation based task transfer for genetic programming

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The general context for this research proposal is that of genetic programming (GP) as applied to learning decision making policies for agents operating in environments with delayed payoff (or reinforcement learning). The specific focus of the proposal lies in developing a framework for systematically scaling GP to more difficult versions of tasks with delayed payoff than have previously been considered. In particular we are interested in scenarios in which solutions for a simpler initial `source' task are then `transferred' to a more difficult but related (target) task; or a form of transfer learning. The insight of this work is to make use of the capability of GP to identify solutions that make use of subsets of state variables. This then provides the basis for redeploying solutions discovered under the source task such that more difficult tasks can be solved. The potential benefits of adopting such an approach are that: 1) it is not necessary to continuously rediscover policies from scratch; 2) increased success in / or better solutions to the ultimate target task; and 3) lower computational overhead as measured against finding solutions to each task.