TrailBlazer extends Monte-Carlo sampling to alternating max and expectation steps in MDPs, delivering sample-complexity bounds that scale with the number of near-optimal states rather than the full state space.
Princeton University Press, Princeton, NJ
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A new parametric framework shows that higher forecast uncertainty consistently shortens the optimal planning horizon for battery energy arbitrage across different battery designs.
citing papers explorer
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Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning
TrailBlazer extends Monte-Carlo sampling to alternating max and expectation steps in MDPs, delivering sample-complexity bounds that scale with the number of near-optimal states rather than the full state space.
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Mapping High-Performance Regions in Battery Scheduling across Data Uncertainty, Battery Design, and Planning Horizons
A new parametric framework shows that higher forecast uncertainty consistently shortens the optimal planning horizon for battery energy arbitrage across different battery designs.