Discovering Symbolic Policy for Building Control using Reinforcement Learning
Published in IFAC WC 2023, 2023
We propose a learning framework for interpretable HVAC control in buildings using deep reinforcement learning (DRL).
Publications
Landscape Optimization for Prescribed Burns in Wildfire Mitigation Planning
Published in COMPASS2022, 2022
we model the wildfire mitigation problem of selecting where to perform a set of prescribed burns across a large landscape into a multi-objective optimization problem. We build a workflow for later researchers to follow when dealing with similar problem.
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Temporal Induced Self-Play for Stochastic Bayesian Games
Published in IJCAI2021, 2021
We proposed a temporal induced based self-play algorithm for stochastic Bayesian games. We showed that with our algorithm, we can efficiently converge close to sequential perfect bayesian equilibrium, which make the learned policy robust.
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Data-Driven Multimodal Patrol Planning for Anti-poaching
Published in IAAI2021, 2021
We used machine learning models to predict the threats and then use a novel mixed-integer linear programming to plan the route in a mix of driving and walking patrols.
When to Follow the Tip: Security Games with Strategic Informants
Published in IJCAI-PRICAI 2020, 2020
We introduced a new player – a strategic informant, who can observe and report upcoming attacks – to the defender-attacker security game setting.
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Bi-level Actor-Critic for Multi-agent Coordination
Published in AAAI2020, 2020
We proposed a novel bi-level actor-critic learning method for multi-agent reinforcement learning that allows agents to have different knowledge base, while their actions still can be executed simultaneously and distributedly, and result in Stackelberg equilibrium as the solution.
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