Humans are, and for the foreseeable future remain our best and only
example of true intelligence. In comparison, even advanced robots are
still embarrassingly stupid. Consequently, one popular approach for
imparting intelligent behaviors to robots and other machines abstracts
models of human control strategy (HCS), learned directly from human
control data. This type of approach can be broadly classified as
"learning through observation." A competing approach, which builds up
complex behaviors through exploration and optimization over time, is
reinforcement learning. We seek to unite these two approaches and show
that each approach, in fact, complements the other. Specifically, we
propose a new algorithm, rooted in reinforcement learning, for
stabilizing learned models of human control strategy. In this paper,
we first describe the real-time driving simulator which we have
developed for investigating human control strategies. Next, we
motivate and describe our framework for modeling human control
strategies. We then illustrate how the resulting HCS models can be
stabilized through reinforcement learning and finally report some
positive experimental results.
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