Imitation and Reinforcement Learning in Agents with Heterogeneous Actions

Bob Price and Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: {price,cebly}

Implicit imitation can accelerate reinforcement learning (RL) by augmenting the Bellman equations with information from the observation of expert agents (mentors). We propose two extensions that permit imitation of agents with heterogeneous actions: feasibility testing, which detects infeasible mentor actions, and k-step repair, which searhes for plans that approximate infeasible actions. We show empirically that both of extensions allow imitatation agents to converge more quickly in in the presence of heterogeneous actions.

To appear, AI'2001 (14th Canadian Conference on AI)

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