5 papers
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…
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…
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…