7 papers
Quantifying Potential Observation Missingness in Inverse Reinforcement Learning
Leo Benac, Abhishek Sharma, Alihan Huyuk +1
Inverse reinforcement learning (IRL), which infers reward functions from demonstrations, is a valuable tool for modeling and understanding decision-making behavior. Many variants o…
A Benchmark for Multi-Party Negotiation Games from Real Negotiation Data
Leo Benac, Jonas Raedler, Zilin Ma +1
Many real-world multi-party negotiations unfold as sequences of binding, action-level commitments rather than a single final outcome, yet this regime remains under-studied in exist…
Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning
Inko Bovenzi, Adi Carmel, Michael Hu +5
In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application of Inverse Reinforcement Learning…
Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectories
Leo Benac, Abhishek Sharma, Sonali Parbhoo +1
We consider the problem of estimating the transition dynamics from near-optimal expert trajectories in the context of offline model-based reinforcement learning. We develop a…
Decision-Point Guided Safe Policy Improvement
Abhishek Sharma, Leo Benac, Sonali Parbhoo +1
Within batch reinforcement learning, safe policy improvement (SPI) seeks to ensure that the learnt policy performs at least as well as the behavior policy that generated the datase…
Bayesian Inverse Transition Learning for Offline Settings
Leo Benac, Sonali Parbhoo, Finale Doshi-Velez
Offline Reinforcement learning is commonly used for sequential decision-making in domains such as healthcare and education, where the rewards are known and the transition dynamics…
Risk averse non-stationary multi-armed bandits
Leo Benac, Frédéric Godin
This paper tackles the risk averse multi-armed bandits problem when incurred losses are non-stationary. The conditional value-at-risk (CVaR) is used as the objective function. Two…