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Found 6 papers, 2 papers with code
CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation
Our empirical results show that when the reward model is misspecified and the annotations are imperfect, it is most beneficial to use the annotations only in the DM portion of a DR estimator.
Adaptive Interventions with User-Defined Goals for Health Behavior Change
Tailoring advice to a person's unique goals, preferences, and life circumstances is a critical component of health coaching that has been underutilized in adaptive algorithms for mobile health interventions.
Kernel Density Bayesian Inverse Reinforcement Learning
In this work, we incorporate existing domain-specific data to achieve better posterior concentration rates.
Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation
Inverse reinforcement learning (IRL) methods attempt to recover the reward function of an agent by observing its behavior.
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
CFQI uses a compositional $Q$-value function with separate modules for each task variant, allowing it to take advantage of shared knowledge while learning distinct policies for each variant.
Nested Policy Reinforcement Learning for Clinical Decision Support
Off-policy reinforcement learning (RL) has proven to be a powerful framework for guiding agents' actions in environments with stochastic rewards and unknown or noisy state dynamics.
