Accelerating Reinforcement Learning through Implicit Imitation

TL;DR

This paper introduces a formal model of implicit imitation to accelerate reinforcement learning by leveraging demonstrations from mentors, improving learning speed and convergence in multiagent environments.

Contribution

It presents a novel formal model of implicit imitation that enhances reinforcement learning efficiency, including instantiations for agents with identical and different capabilities.

Findings

Implicit imitation significantly speeds up learning.

Integration with prioritized sweeping improves convergence.

Observation of multiple mentors yields better performance.

Abstract

Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent's ability to learn useful behaviors by making intelligent use of the knowledge implicit in behaviors demonstrated by cooperative teachers or other more experienced agents. We propose and study a formal model of implicit imitation that can accelerate reinforcement learning dramatically in certain cases. Roughly, by observing a mentor, a reinforcement-learning agent can extract information about its own capabilities in, and the relative value of, unvisited parts of the state space. We study two specific instantiations of this model, one in which the learning agent and the mentor have identical abilities, and one designed to deal with agents and mentors with different action sets. We illustrate the benefits of implicit imitation 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 and possible interactions, we briefly comment on extensions of the model that relax these restricitions.