Philip D Loewen — accepted AI-conference papers

2025

DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation

Shuyuan Wang, Philip D Loewen, Michael Forbes, Bhushan Gopaluni, Wei Pan

ICML 2025poster

While differentiable control has emerged as a powerful paradigm combining model-free flexibility with model-based efficiency, the iterative Linear Quadratic Regulator (iLQR) remains underexplored as a differentiable component. The scalability of differentiating through extended iterations and horizo…

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2024

Guiding Reinforcement Learning with Incomplete System Dynamics

Shuyuan Wang, Jingliang Duan, Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni +1

IROS 2024poster

Model-free reinforcement learning (RL) is inherently a reactive method, operating under the assumption that it starts with no prior knowledge of the system and entirely depends on trial-and-error for learning. This approach faces several challenges, such as poor sample efficiency, generalization, an…

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