Optimal Reward Shaping: Autonomous Car Parking Case Study

• URL: http://arxiv.org/abs/2607.23617v1
• Date: Sun, 26 Jul 2026 11:49:23 GMT
• Title: Optimal Reward Shaping: Autonomous Car Parking Case Study
• Abstract summary: We present a parameterized reward shaping framework featuring alignment feedback, drive-direction switch regularization, and an aligned episode termination mechanism evaluated on an autonomous parallel parking task.<n>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.
• Score: 1.1458853556386799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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