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Found 7 papers, 5 papers with code
Ranked Prioritization of Groups in Combinatorial Bandit Allocation
Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land.
Robust Restless Bandits: Tackling Interval Uncertainty with Deep Reinforcement Learning
To make RMABs more useful in settings with uncertain dynamics: (i) We introduce the Robust RMAB problem and develop solutions for a minimax regret objective when transitions are given by interval uncertainties; (ii) We develop a double oracle algorithm for solving Robust RMABs and demonstrate its effectiveness on three experimental domains; (iii) To enable our double oracle approach, we introduce RMABPPO, a novel deep reinforcement learning algorithm for solving RMABs.
Robust Reinforcement Learning Under Minimax Regret for Green Security
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.
Envisioning Communities: A Participatory Approach Towards AI for Social Good
We argue that AI for social good ought to be assessed by the communities that the AI system will impact, using as a guide the capabilities approach, a framework to measure the ability of different policies to improve human welfare equity.
Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery
To ensure under-resourced parks have access to meaningful poaching predictions, we introduce the use of publicly available remote sensing data to extract features for parks.
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security
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).
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations
We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia.
