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Found 2 papers, 1 papers with code
Discrete Probabilistic Inference as Control in Multi-path Environments
We consider the problem of sampling from a discrete and structured distribution as a sequential decision problem, where the objective is to find a stochastic policy such that objects are sampled at the end of this sequential process proportionally to some predefined reward.
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization
The problem can be framed as a global optimization problem where the objective is an expensive black-box function such that we can query large batches restricted with a limitation of a low number of rounds.
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