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Imitation and Reinforcement Learning with Heterogeneous Actions

By Bob Price and Craig Boutilier

Abstract

We study the problem of accelerating reinforcement learning through the observation and implicit imitation of expert agents (mentors) acting in the same domain. In this paper, we consider problems that arise when the learner and mentor have heterogeneous actions. We extend an earlier implicit imitation model to allow for feasibility testing (determining whether a specific mentor action can be duplicated) and repair (discovering a "plan" that simulates a mentor's trajectory) and demonstrate empirically that both of these components allow learning agents to learn much more readily than standard RL agents and implicit imitation agents without these extended capabilities

Year: 2007
OAI identifier: oai:CiteSeerX.psu:10.1.1.19.1996
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