Permutation based task transfer for genetic programming

这项研究建议的一般背景是，遗传编程（GP）适用于学习决策政策的代理人在延迟支付（或强化学习）的环境中运行。该提案的具体重点在于开发一个框架，以系统地扩展GP到比以前考虑的更困难的延迟回报任务版本。特别是，我们感兴趣的场景中，一个简单的初始“源”任务的解决方案，然后“转移”到一个更困难的，但相关的（目标）任务;或一种形式的迁移学习。这项工作的见解是利用GP的能力，以确定解决方案，利用状态变量的子集。然后，这为重新部署在源任务下发现的解决方案提供了基础，这样就可以解决更困难的任务。采用这种方法的潜在好处是：1）没有必要从头开始不断地重新发现策略; 2）增加最终目标任务的成功/或更好的解决方案;以及3）如针对找到每个任务的解决方案所测量的更低的计算开销。

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.