Optimal Reward Shaping: Autonomous Car Parking Case Study
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 Bay...
| Authors | Emre Özkaya, Nicolas R. Gauger |
|---|---|
| Primary category | cs.LG |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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