Speeding Up Path Planning via Reinforcement Learning in MCTS for Automated Parking

• URL: http://arxiv.org/abs/2403.17234v1
• Date: Mon, 25 Mar 2024 22:21:23 GMT
• Title: Speeding Up Path Planning via Reinforcement Learning in MCTS for Automated Parking
• Authors: Xinlong Zheng, Xiaozhou Zhang, Donghao Xu,
• Abstract summary: 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, we are able to model a value estimator and a policy generator for given states.
• Score: 3.750010944080163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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