通过智能奖励设计，让自动驾驶泊车更稳定高效。
Optimal Reward Shaping: Autonomous Car Parking Case Study
- 用动态反馈调节奖励，避免车辆卡住或过于保守。
- 联合优化环境参数与算法超参，成功率和轨迹平滑度显著提升。
- 适合研究强化学习奖励设计或自动驾驶控制的读者。
在非完整约束下设计有效的无模型强化学习奖励函数仍是一个持续挑战，常导致严重局部极小值，如策略瘫痪或过度保守避障。本文提出一种参数化奖励塑造框架，包含覆盖门控对齐反馈、驱动方向切换正则化及对齐的回合终止机制，在自动驾驶平行泊车任务上进行评估。关键发现：环境奖励参数与算法超参数存在深层耦合，需联合元优化才能实现稳定收敛。通过基于代理的贝叶斯优化，所提出的共优化深度Q网络（DQN）代理解决了典型的控制失败模式，在成功率和轨迹平滑度上显著优于未校准基线。
原文摘要 · Abstract (English)
Designing effective reward functions for model-free reinforcement learning under non-holonomic constraints remains a persistent challenge, often resulting in severe local minima such as policy paralysis or over-conservative hazard avoidance. In this work, we present a parameterized reward shaping framework featuring coverage-gated alignment feedback, drive-direction switch regularization, and an aligned episode termination mechanism evaluated on an autonomous parallel parking task. Crucially, we show that environmental reward parameters and algorithmic hyperparameters are deeply co-dependent, requiring joint meta-optimization to achieve stable convergence. By employing surrogate-based Bayesian optimization, our co-optimized Deep Q-Network (DQN) agent resolves characteristic control failure modes, significantly outperforming uncalibrated baselines across both success rate and trajectory smoothness.
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