Apprentice learning and reinforcement learning are methods that have each been developed in order to endow computerized agents with the capacity to learn to perform multiple-step tasks, such as problem-solving tasks and control tasks. To achieve this end, each method takes differing approaches, with disparate assumptions, objectives, and algorithms. In apprentice learning, the autonomous agent tries to mimic a training agent's problem-solving behavior, learning based on examples of the trainer's action choices. In an attempt to learn to perform its task optimally, the learner in reinforcement learning changes its behavior based on scalar feedback about the consequences of its own actions. We demonstrate that a careful integration of the two learning methods can produce a more powerful method than either one alone. An argument based on the characteristics of the individuals maintains that a hybrid will be an improvement because of the complimentary strengths of its constituents. Although existing hybrids of apprentice learning and reinforcement learning perform better than their individual components, those hybrids have left many questions unanswered. We consider the following questions in this dissertation. How do the learner and trainer interact during training? How does the learner assimilate the trainer's expertise? How does the proficiency of the trainer affect the learner's ability to perform the task? And, when during training should the learner acquire information from the trainer? In our quest for answers, we develop the A scSK FOR H scELP integrated approach, and use it in our empirical study. With the new integrated approach, the learning agent is significantly faster at learning to perform optimally than learners employing either apprentice learning alone or reinforcement learning alone. The study indicates further that the learner can learn to perform optimally even when its trainer cannot; thus, the learner can outperform its trainer. Two strategies for determining when to acquire the trainer's aid show that simple approaches work well. The results of the study demonstrate that the A scSK FOR H scELP approach is effective for integrating apprentice learning and reinforcement learning, and support the conclusion that an integrated approach can be better than its individual components.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:dissertations-2771 |
| Date | 01 January 1996 |
| Creators | Clouse, Jeffery Allen |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Language | English |
| Detected Language | English |
| Type | text |
| Source | Doctoral Dissertations Available from Proquest |
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