  In problem-solving, we humans can come up with multiple novel solutions to
the same problem. However, reinforcement learning algorithms can only produce a
set of monotonous policies that maximize the cumulative reward but lack
diversity and novelty. In this work, we address the problem of generating novel
policies in reinforcement learning tasks. Instead of following the
multi-objective framework used in existing methods, we propose to rethink the
problem under a novel perspective of constrained optimization. We first
introduce a new metric to evaluate the difference between policies and then
design two practical novel policy generation methods following the new
perspective. The two proposed methods, namely the Constrained Task Novel
Bisector (CTNB) and the Interior Policy Differentiation (IPD), are derived from
the feasible direction method and the interior point method commonly known in
the constrained optimization literature. Experimental comparisons on the MuJoCo
control suite show our methods can achieve substantial improvement over
previous novelty-seeking methods in terms of both the novelty of policies and
their performances in the primal task.
