Active Learning within Constrained Environments through
Imitation of an Expert Questioner
Abstract
Active learning agents typically employ a query
selection algorithm which solely considers the
agent’s learning objectives. However, this may be
insufficient in more realistic human domains. This
work uses imitation learning to enable an agent in
a constrained environment to concurrently reason
about both its internal learning goals and environmental constraints externally imposed, all within
its objective function. Experiments are conducted
on a concept learning task to test generalization
of the proposed algorithm to different environmental conditions and analyze how time and resource
constraints impact efficacy of solving the learning
problem. Our findings show the environmentallyaware learning agent is able to statistically outperform all other active learners explored under most
of the constrained conditions. A key implication
is adaptation for active learning agents to more realistic human environments, where constraints are
often externally imposed on the learner