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Beyond Expected Values: Risk-Sensitive Planning with Distributional Monte-Carlo Tree Search

Trinh, Quynh et al.
arXiv (All) · Papers
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machine-learning
Monte-Carlo Tree Search (MCTS) typically stores only scalar mean estimates, which makes it poorly suited for tail-risk objectives such as conditional value-at-risk (CVaR). We introduce Categorical Thompson Sampling with Optimism (CATSO) and Particle Thompson Sampling with Optimism (PATSO), two distr
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