Implicit Imitation in Multiagent Reinforcement Learning

Bob Price and Craig Boutilier
Department of Computer Science
University of British Columbia
Vancouver, BC, CANADA, V6T 1Z4
email: {price,cebly}

Imitation is actively being studied as an effective means of learning in multi-agent environments. It allows an agent to learn how to act well (perhaps optimally) by observing the actions of cooperative teachers or more experienced agents. We propose a straightforward imitation mechanism called model extraction that can be integrated easily into standard model-based reinforcement learning algorithms. Roughly, by observing a mentor with similar capabilities, an agent can extract information about its own capabilities in unvisited parts of state space. The extracted information can accelerate learning dramatically. We illustrate the benefits of model extraction by integrating it with prioritized sweeping, and demonstrating improved performance and convergence through observation of single and multiple mentors. Though we make some stringent assumptions regarding observability, possible interactions and common abilities, we briefly comment on extensions to the model that relax these.

To appear, ICML-99

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