mlsanityData to 14 Oct 2026

Optimal Reward Shaping: Autonomous Car Parking Case Study

arXiv 2607.23617 ↗First posted 26 Jul 2026cs.LG math.OCcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 9 references that are in the map. First posted on arXiv 26 Jul 2026.

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  1. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 171

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  8. 2024Speeding Up Path Planning via Reinforcement Learning in MCTS for Automated Parking