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Experiments on stochastic planning benchmarks show that distributional Q-edges improve lower-tail return and reduce catastrophic outcomes when mean-optimal and risk-sensitive behavior differ, and two distributional MCTS algorithms that maintain finite-support empirical backup laws at Q-edges are introduced.
We propose Variance Driven Exploration (VarDE), a principled approach for pure exploration in highly stochastic environments, where the exploration process is dominated by stochastic variance. VarDE is built on a fundamental principle: sampling effort should be allocated to minimize the uncertainty of the final decisio...
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...
This work proves that the forward potential coincides with geometric discounted residual propagation and shows that GTA-PS gives active priority instantly to all states, and introduces the Graph Topology Augmentation framework, which employ the graph's resolvent and its diffusion semantic, to augment the inquired signa...
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.
The resulting SecondOrderSmoothCruiser achieves oracle complexity for fixed OT parameters, and is related to the OT, entropy-regularized, and unregularized objectives through explicit regularization-bias bounds.
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