Apr 26, 2024 · A lifelong policy learning (LLPL) framework is proposed in this paper, which extends the IL scheme with lifelong learning (LLL).
The results show that the proposed LLPL framework can continuously improve the policy performance with collected incremental driving data, and achieves the best ...
Model-free learning-based control methods have recently shown significant advantages over traditional control methods in avoiding complex vehicle ...
Apr 28, 2024 · This paper presents a lifelong policy learning (LLPL) framework that combines imitation learning (IL) and lifelong learning (LLL) to address ...
探究'Beyond Imitation: A Life-Long Policy Learning Framework for Path Tracking Control of Autonomous Driving' 的科研主题。它们共同构成独一无二的指纹。
Beyond Imitation: A Life-long Policy Learning Framework for Path Tracking Control of Autonomous Driving. C Gong, C Lu, Z Li, Z Liu, J Gong, X Chen. IEEE ...
Sep 12, 2024 · This paper proposes a life-long adaptive path tracking policy learning method for autonomous vehicles that can self-evolve and self-adapt ...
A novel feedback synthesizer is proposed for data augmentation that allows the agent to gain more driving experience in various previously unseen environments.
Imitation learning is a powerful approach for learning autonomous driving ... Life-Long Policy Learning Framework for Path Tracking Control of Autonomous Driving.
首先，介绍了一种新颖的基于IL的无模型控制策略学习方法，用于路径跟踪。即使有不完美的演示，最优控制策略也可以直接从历史驾驶数据中学习。其次，通过使用LLL方法，可以安全地 ...
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