Bayesian Inverse Transition Learning for Offline Settings. (arXiv:2308.05075v1 [cs.LG])

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. A key challenge for all tasks is how to learn a reliable estimate
of the transition dynamics $T$ that produce near-optimal policies that are safe
enough so that they never take actions that are far away from the best action
with respect to their value …

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