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Monte-Carlo tree search with uncertainty propagation via optimal transport
We introduce a novel backup operator that computes value nodes as the Wasserstein barycenter of their action-value children nodes; thus, propagating the uncertainty of the estimate across the tree to the root node.
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search
In this work, we propose two methods for improving the convergence rate and exploration based on a newly introduced backup operator and entropy regularization.
Convex Regularization in Monte-Carlo Tree Search
Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making.
Generalized Mean Estimation in Monte-Carlo Tree Search
Finally, we empirically demonstrate the effectiveness of our method in well-known MDP and POMDP benchmarks, showing significant improvement in performance and convergence speed w. r. t.
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