Confounding Robust Continuous Control via Automatic Reward Shaping

• URL: http://arxiv.org/abs/2602.10305v1
• Date: Tue, 10 Feb 2026 21:23:12 GMT
• Title: Confounding Robust Continuous Control via Automatic Reward Shaping
• Authors: Mateo Juliani, Mingxuan Li, Elias Bareinboim,
• Abstract summary: We propose to automatically learn a reward shaping function for continuous control problems from offline datasets.<n>Our method builds upon the recently proposed causal Bellman equation to learn a tight upper bound on the optimal state values.<n>Our work marks a solid first step towards confounding robust continuous control from a causal perspective.
• Score: 48.93769483870838
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reward shaping has been applied widely to accelerate Reinforcement Learning (RL) agents' training. However, a principled way of designing effective reward shaping functions, especially for complex continuous control problems, remains largely under-explained. In this work, we propose to automatically learn a reward shaping function for continuous control problems from offline datasets, potentially contaminated by unobserved confounding variables. Specifically, our method builds upon the recently proposed causal Bellman equation to learn a tight upper bound on the optimal state values, which is then used as the potentials in the Potential-Based Reward Shaping (PBRS) framework. Our proposed reward shaping algorithm is tested with Soft-Actor-Critic (SAC) on multiple commonly used continuous control benchmarks and exhibits strong performance guarantees under unobserved confounders. More broadly, our work marks a solid first step towards confounding robust continuous control from a causal perspective. Code for training our reward shaping functions can be found at https://github.com/mateojuliani/confounding_robust_cont_control.

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