Defines responsive distributions for G-normal random variables and proposes a convergent coupled trinomial tree algorithm to compute G-expectations and sample the induced laws.
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AGMCTS augments MCTS with action-score gradients for particle beliefs, a Multiple Importance Sampling tree for reuse, and Area Formula gradients for smooth models, outperforming prior sample-based solvers on continuous benchmarks.
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Responsive Distribution of G-normal Random Variables
Defines responsive distributions for G-normal random variables and proposes a convergent coupled trinomial tree algorithm to compute G-expectations and sample the induced laws.
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Action-Gradient Monte Carlo Tree Search for Non-Parametric Continuous (PO)MDPs
AGMCTS augments MCTS with action-score gradients for particle beliefs, a Multiple Importance Sampling tree for reuse, and Area Formula gradients for smooth models, outperforming prior sample-based solvers on continuous benchmarks.