  In this paper, we address a method that integrates reinforcement learning
into the Monte Carlo tree search to boost online path planning under fully
observable environments for automated parking tasks. Sampling-based planning
methods under high-dimensional space can be computationally expensive and
time-consuming. State evaluation methods are useful by leveraging the prior
knowledge into the search steps, making the process faster in a real-time
system. Given the fact that automated parking tasks are often executed under
complex environments, a solid but lightweight heuristic guidance is challenging
to compose in a traditional analytical way. To overcome this limitation, we
propose a reinforcement learning pipeline with a Monte Carlo tree search under
the path planning framework. By iteratively learning the value of a state and
the best action among samples from its previous cycle's outcomes, we are able
to model a value estimator and a policy generator for given states. By doing
that, we build up a balancing mechanism between exploration and exploitation,
speeding up the path planning process while maintaining its quality without
using human expert driver data.
