110 citations · 183 across the 17 of their papers we have counts for
10 papers · 1 filter
Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions
Lingkai Kong, Anagha Satish, Hezi Jiang +6
Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed by complex feasibility constraint…
Generative AI Against Poaching: Latent Composite Flow Matching for Wildlife Conservation
Lingkai Kong, Haichuan Wang, Charles A. Emogor +3
Poaching poses significant threats to wildlife and biodiversity. A valuable step in reducing poaching is to forecast poacher behavior, which can inform patrol planning and other co…
Reinforcement learning with combinatorial actions for coupled restless bandits
Lily Xu, Bryan Wilder, Elias B. Khalil +1
Reinforcement learning (RL) has increasingly been applied to solve real-world planning problems, with progress in handling large state spaces and time horizons. However, a key bott…
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits
Biyonka Liang, Lily Xu, Aparna Taneja +2
Public health programs often provide interventions to encourage program adherence, and effectively allocating interventions is vital for producing the greatest overall health outco…
Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits
Siddhartha Banerjee, Sean R. Sinclair, Milind Tambe +2
Most real-world deployments of bandit algorithms exist somewhere in between the offline and online set-up, where some historical data is available upfront and additional data is co…
Optimistic Whittle Index Policy: Online Learning for Restless Bandits
Kai Wang, Lily Xu, Aparna Taneja +1
Restless multi-armed bandits (RMABs) extend multi-armed bandits to allow for stateful arms, where the state of each arm evolves restlessly with different transitions depending on w…
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning
Jackson A. Killian, Lily Xu, Arpita Biswas +1
We introduce robustness in \textit{restless multi-armed bandits} (RMABs), a popular model for constrained resource allocation among independent stochastic processes (arms). Nearly…
Robust Reinforcement Learning Under Minimax Regret for Green Security
Lily Xu, Andrew Perrault, Fei Fang +2
Green security domains feature defenders who plan patrols in the face of uncertainty about the adversarial behavior of poachers, illegal loggers, and illegal fishers. Importantly,…
Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery
Rachel Guo, Lily Xu, Drew Cronin +3
Illegal wildlife poaching is driving the loss of biodiversity. To combat poaching, rangers patrol expansive protected areas for illegal poaching activity. However, rangers often ca…
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security
Lily Xu, Elizabeth Bondi, Fei Fang +3
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 are…