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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 9: AAAI-22 Technical Tracks 9

Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs

February 1, 2023

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Authors

Siow Meng Low

Singapore Management University


Akshat Kumar

Singapore Management University


Scott Sanner

University of Toronto


DOI:

10.1609/aaai.v36i9.21220


Abstract:

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-to-end 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 minorization-maximization 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 sample-efficient 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.

Topics: AAAI

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HOW TO CITE:

Siow Meng Low||Akshat Kumar||Scott Sanner Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs Proceedings of the AAAI Conference on Artificial Intelligence (2022) 9840-9848.

Siow Meng Low||Akshat Kumar||Scott Sanner Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs AAAI 2022, 9840-9848.

Siow Meng Low||Akshat Kumar||Scott Sanner (2022). Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs. Proceedings of the AAAI Conference on Artificial Intelligence, 9840-9848.

Siow Meng Low||Akshat Kumar||Scott Sanner. Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.9840-9848.

Siow Meng Low||Akshat Kumar||Scott Sanner. 2022. Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs. "Proceedings of the AAAI Conference on Artificial Intelligence". 9840-9848.

Siow Meng Low||Akshat Kumar||Scott Sanner. (2022) "Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs", Proceedings of the AAAI Conference on Artificial Intelligence, p.9840-9848

Siow Meng Low||Akshat Kumar||Scott Sanner, "Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs", AAAI, p.9840-9848, 2022.

Siow Meng Low||Akshat Kumar||Scott Sanner. "Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.9840-9848.

Siow Meng Low||Akshat Kumar||Scott Sanner. "Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 9840-9848.

Siow Meng Low||Akshat Kumar||Scott Sanner. Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs. AAAI[Internet]. 2022[cited 2023]; 9840-9848.


ISSN: 2374-3468


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Copyright 2022, Association for the Advancement of
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