  The reinforcement learning problem of finding a control policy that minimizes
the minimum time objective for the Mountain Car environment is considered.
Particularly, a class of parameterized nonlinear feedback policies is optimized
over to reach the top of the highest mountain peak in minimum time. The
optimization is carried out using quasi-Stochastic Gradient Descent (qSGD)
methods. In attempting to find the optimal minimum time policy, a new
parameterized policy approach is considered that seeks to learn an optimal
policy parameter for different regions of the state space, rather than rely on
a single macroscopic policy parameter for the entire state space. This
partitioned parameterized policy approach is shown to outperform the uniform
parameterized policy approach and lead to greater generalization than prior
methods, where the Mountain Car became trapped in circular trajectories in the
state space.
