  We present a Reinforcement Learning (RL) solution to the view planning
problem (VPP), which generates a sequence of view points that are capable of
sensing all accessible area of a given object represented as a 3D model. In
doing so, the goal is to minimize the number of view points, making the VPP a
class of set covering optimization problem (SCOP). The SCOP is NP-hard, and the
inapproximability results tell us that the greedy algorithm provides the best
approximation that runs in polynomial time. In order to find a solution that is
better than the greedy algorithm, (i) we introduce a novel score function by
exploiting the geometry of the 3D model, (ii) we model an intuitive human
approach to VPP using this score function, and (iii) we cast VPP as a Markovian
Decision Process (MDP), and solve the MDP in RL framework using well-known RL
algorithms. In particular, we use SARSA, Watkins-Q and TD with function
approximation to solve the MDP. We compare the results of our method with the
baseline greedy algorithm in an extensive set of test objects, and show that we
can out-perform the baseline in almost all cases.
