Sample-efficient iterative lower bound optimization of deep reactive policies for planning in continuous MDPs

• Main Authors: LOW, Siow Meng, KUMAR, Akshat, SANNER, Scott
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/7724; https://ink.library.smu.edu.sg/context/sis_research/article/8727/viewcontent/21220_Article_Text_25233_1_2_20220628.pdf
• Institution: Singapore Management University

Recent advances in deep learning have enabled optimization of deep reactive policies (DRPs) for continuous MDP planning by encoding a parametric policy as a deep neural network and exploiting automatic differentiation in an end-toend model-based gradient descent framework. This approach has proven effective for optimizing DRPs in nonlinear continuous MDPs, but it requires a large number of sampled trajectories to learn effectively and can suffer from high variance in solution quality. In this work, we revisit the overall model-based DRP objective and instead take a minorizationmaximization perspective to iteratively optimize the DRP w.r.t. a locally tight lower-bounded objective. This novel formulation of DRP learning as iterative lower bound optimization (ILBO) is particularly appealing because (i) each step is structurally easier to optimize than the overall objective, (ii) it guarantees a monotonically improving objective under certain theoretical conditions, and (iii) it reuses samples between iterations thus lowering sample complexity. Empirical evaluation confirms that ILBO is significantly more sampleefficient than the state-of-the-art DRP planner and consistently produces better solution quality with lower variance. We additionally demonstrate that ILBO generalizes well to new problem instances (i.e., different initial states) without requiring retraining.