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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. |
