Summary of Speeding Up Path Planning Via Reinforcement Learning in Mcts For Automated Parking, by Xinlong Zheng et al.
Speeding Up Path Planning via Reinforcement Learning in MCTS for Automated Parking
by Xinlong Zheng, Xiaozhou Zhang, Donghao Xu
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a method that integrates reinforcement learning into Monte Carlo tree search to improve online path planning for automated parking tasks in fully observable environments. The traditional sampling-based planning methods can be computationally expensive and time-consuming, making it challenging to achieve real-time performance. To overcome this limitation, the authors propose a reinforcement learning pipeline that iteratively learns the value of a state and the best action among samples from its previous cycle’s outcomes. This approach enables the construction of a value estimator and a policy generator for given states, allowing for a balancing mechanism between exploration and exploitation. The proposed method is designed to speed up the path planning process while maintaining its quality without relying on human expert driver data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated parking tasks are complex and require efficient path planning. This paper introduces a new approach that combines reinforcement learning with Monte Carlo tree search to improve online path planning. The goal is to make the system faster and more effective, without using expert driver data. The method works by learning from previous outcomes and making better decisions over time. This allows for a balance between exploring new options and sticking with what works best. The result is a more efficient and reliable parking system. |
Keywords
» Artificial intelligence » Reinforcement learning
