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Tree-based Monte-Carlo Tree Search (MCTS) duplicates the same state when it is reached through different trajectories, which can waste simulations in stochastic MDPs. We introduce Graph-Based Stochastic-Power-UCT (GS-Power-UCT), which shares states reached at the same planning depth while keeping separate values for st...
A new robust variant of MCTS that mitigates dynamical model ambiguities to bridge the gap between simulation-based planning and real-world deployment and empirical evidence is provided that this method achieves robust performance in planning problems even under significant ambiguity in the underlying reward distributio...
A novel MCTS algorithm, \Algname, designed for continuous, stochastic MDPs, that integrates a power mean as a value backup operator, alongside a polynomial exploration bonus to address the non-stationarity inherent in continuous action spaces.
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