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Bayesian Inverse Transition Learning for Offline Settings
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 $T$ must be estimated on the basis of batch data.
Risk averse non-stationary multi-armed bandits
Simulation experiments assess the performance of the arm selection algorithms based on the two novel estimation approaches, and such policies are shown to outperform naive benchmarks not taking non-stationarity into account.
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