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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Deep Reactive Policies for Planning in Stochastic Nonlinear Domains

February 1, 2023

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Authors

Thiago P. Bueno

University of São Paulo


Leliane N. de Barros

University of Sao Paulo


Denis D. Mauá

University of Sao Paulo


Scott Sanner

University of Toronto


DOI:

10.1609/aaai.v33i01.33017530


Abstract:

Recent advances in applying deep learning to planning have shown that Deep Reactive Policies (DRPs) can be powerful for fast decision-making in complex environments. However, an important limitation of current DRP-based approaches is either the need of optimal planners to be used as ground truth in a supervised learning setting or the sample complexity of high-variance policy gradient estimators, which are particularly troublesome in continuous state-action domains. In order to overcome those limitations, we introduce a framework for training DRPs in continuous stochastic spaces via gradient-based policy search. The general approach is to explicitly encode a parametric policy as a deep neural network, and to formulate the probabilistic planning problem as an optimization task in a stochastic computation graph by exploiting the re-parameterization of the transition probability densities; the optimization is then solved by leveraging gradient descent algorithms that are able to handle non-convex objective functions. We benchmark our approach against stochastic planning domains exhibiting arbitrary differentiable nonlinear transition and cost functions (e.g., Reservoir Control, HVAC and Navigation). Results show that DRPs with more than 125,000 continuous action parameters can be optimized by our approach for problems with 30 state fluents and 30 action fluents on inexpensive hardware under 6 minutes. Also, we observed a speedup of 5 orders of magnitude in the average inference time per decision step of DRPs when compared to other state-of-the-art online gradient-based planners when the same level of solution quality is required.

Topics: AAAI

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HOW TO CITE:

Thiago P. Bueno||Leliane N. de Barros||Denis D. Mauá||Scott Sanner Deep Reactive Policies for Planning in Stochastic Nonlinear Domains Proceedings of the AAAI Conference on Artificial Intelligence (2019) 7530-7537.

Thiago P. Bueno||Leliane N. de Barros||Denis D. Mauá||Scott Sanner Deep Reactive Policies for Planning in Stochastic Nonlinear Domains AAAI 2019, 7530-7537.

Thiago P. Bueno||Leliane N. de Barros||Denis D. Mauá||Scott Sanner (2019). Deep Reactive Policies for Planning in Stochastic Nonlinear Domains. Proceedings of the AAAI Conference on Artificial Intelligence, 7530-7537.

Thiago P. Bueno||Leliane N. de Barros||Denis D. Mauá||Scott Sanner. Deep Reactive Policies for Planning in Stochastic Nonlinear Domains. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.7530-7537.

Thiago P. Bueno||Leliane N. de Barros||Denis D. Mauá||Scott Sanner. 2019. Deep Reactive Policies for Planning in Stochastic Nonlinear Domains. "Proceedings of the AAAI Conference on Artificial Intelligence". 7530-7537.

Thiago P. Bueno||Leliane N. de Barros||Denis D. Mauá||Scott Sanner. (2019) "Deep Reactive Policies for Planning in Stochastic Nonlinear Domains", Proceedings of the AAAI Conference on Artificial Intelligence, p.7530-7537

Thiago P. Bueno||Leliane N. de Barros||Denis D. Mauá||Scott Sanner, "Deep Reactive Policies for Planning in Stochastic Nonlinear Domains", AAAI, p.7530-7537, 2019.

Thiago P. Bueno||Leliane N. de Barros||Denis D. Mauá||Scott Sanner. "Deep Reactive Policies for Planning in Stochastic Nonlinear Domains". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.7530-7537.

Thiago P. Bueno||Leliane N. de Barros||Denis D. Mauá||Scott Sanner. "Deep Reactive Policies for Planning in Stochastic Nonlinear Domains". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 7530-7537.

Thiago P. Bueno||Leliane N. de Barros||Denis D. Mauá||Scott Sanner. Deep Reactive Policies for Planning in Stochastic Nonlinear Domains. AAAI[Internet]. 2019[cited 2023]; 7530-7537.


ISSN: 2374-3468


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