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Imitation can be viewed as a means of enhancing learning in multiagentenvironments. It augments an agent's ability to learn useful behaviors bymaking intelligent use of the knowledge implicit in behaviors demonstrated bycooperative teachers or other more experienced agents. We propose and study aformal model of implicit imitation that can accelerate reinforcement learningdramatically in certain cases. Roughly, by observing a mentor, areinforcement-learning agent can extract information about its own capabilitiesin, and the relative value of, unvisited parts of the state space. We study twospecific instantiations of this model, one in which the learning agent and thementor have identical abilities, and one designed to deal with agents andmentors with different action sets. We illustrate the benefits of implicitimitation by integrating it with prioritized sweeping, and demonstratingimproved performance and convergence through observation of single and multiplementors. Though we make some stringent assumptions regarding observability andpossible interactions, we briefly comment on extensions of the model that relaxthese restricitions.

Accelerating Reinforcement Learning through Implicit Imitation