35 citations · 89 across the 43 of their papers we have counts for
20 papers · 1 filter
IRL for Restless Multi-Armed Bandits with Applications in Maternal and Child Health
Gauri Jain, Pradeep Varakantham, Haifeng Xu +3
Public health practitioners often have the goal of monitoring patients and maximizing patients' time spent in "favorable" or healthy states while being constrained to using limited…
Offline Safe Reinforcement Learning Using Trajectory Classification
Ze Gong, Akshat Kumar, Pradeep Varakantham
Offline safe reinforcement learning (RL) has emerged as a promising approach for learning safe behaviors without engaging in risky online interactions with the environment. Most ex…
On Minimizing Adversarial Counterfactual Error in Adversarial RL
Roman Belaire, Arunesh Sinha, Pradeep Varakantham
Deep Reinforcement Learning (DRL) policies are highly susceptible to adversarial noise in observations, which poses significant risks in safety-critical scenarios. The challenge in…
Imitating Cost-Constrained Behaviors in Reinforcement Learning
Qian Shao, Pradeep Varakantham, Shih-Fen Cheng
Complex planning and scheduling problems have long been solved using various optimization or heuristic approaches. In recent years, imitation learning that aims to learn from exper…
SPRINQL: Sub-optimal Demonstrations driven Offline Imitation Learning
Huy Hoang, Tien Mai, Pradeep Varakantham
We focus on offline imitation learning (IL), which aims to mimic an expert's behavior using demonstrations without any interaction with the environment. One of the main challenges…
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning
Huy Hoang, Tien Mai, Pradeep Varakantham
A popular framework for enforcing safe actions in Reinforcement Learning (RL) is Constrained RL, where trajectory based constraints on expected cost (or other cost measures) are em…