Search Results for author: Andrew Perrault

Found 13 papers, 5 papers with code

Learning (Local) Surrogate Loss Functions for Predict-Then-Optimize Problems

no code implementations30 Mar 2022 Sanket Shah, Bryan Wilder, Andrew Perrault, Milind Tambe

Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimisation task that uses its predictions, so that it can perform better on that specific task.

Training Transition Policies via Distribution Matching for Complex Tasks

1 code implementation ICLR 2022 Ju-Seung Byun, Andrew Perrault

We introduce transition policies that smoothly connect lower-level policies by producing a distribution of states and actions that matches what is expected by the next policy.

Hierarchical Reinforcement Learning Q-Learning +1

Robust Reinforcement Learning Under Minimax Regret for Green Security

1 code implementation15 Jun 2021 Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, Milind Tambe

We formulate the problem as a game between the defender and nature who controls the parameter values of the adversarial behavior and design an algorithm MIRROR to find a robust policy.

Decision Making reinforcement-learning

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning

no code implementations NeurIPS 2021 Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe

In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved.

reinforcement-learning

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning

no code implementations NeurIPS 2021 Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe

In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved.

Decision Making reinforcement-learning

Collapsing Bandits and Their Application to Public Health Intervention

1 code implementation NeurIPS 2020 Aditya Mate, Jackson Killian, Haifeng Xu, Andrew Perrault, Milind Tambe

Our main contributions are as follows: (i) Building on the Whittle index technique for RMABs, we derive conditions under which the Collapsing Bandits problem is indexable.

Dual-Mandate Patrols: Multi-Armed Bandits for Green Security

2 code implementations14 Sep 2020 Lily Xu, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang, Milind Tambe

Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i. e., patrollers), who must patrol vast areas to protect from attackers (e. g., poachers or illegal loggers).

Multi-Armed Bandits

Collapsing Bandits and Their Application to Public Health Interventions

no code implementations5 Jul 2020 Aditya Mate, Jackson A. Killian, Haifeng Xu, Andrew Perrault, Milind Tambe

(ii) We exploit the optimality of threshold policies to build fast algorithms for computing the Whittle index, including a closed-form.

Automatically Learning Compact Quality-aware Surrogates for Optimization Problems

2 code implementations NeurIPS 2020 Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe

Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values.

Portfolio Optimization

AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline

no code implementations16 Dec 2019 Andrew Perrault, Fei Fang, Arunesh Sinha, Milind Tambe

With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems.

Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning

no code implementations20 Nov 2019 Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Milind Tambe

To solve the online problem with a hard bound on risk, we formulate it as a Reinforcement Learning (RL) problem with constraints on the action space (hard bound on risk).

reinforcement-learning

End-to-End Game-Focused Learning of Adversary Behavior in Security Games

no code implementations3 Mar 2019 Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, Milind Tambe

Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary.

Exploring Strategy-Proofness, Uniqueness, and Pareto Optimality for the Stable Matching Problem with Couples

no code implementations13 May 2015 Andrew Perrault, Joanna Drummond, Fahiem Bacchus

The Stable Matching Problem with Couples (SMP-C) is a ubiquitous real-world extension of the stable matching problem (SMP) involving complementarities.

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