2 citations · 3 across the 5 of their papers we have counts for
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cs.AI2024
AlphaZeroES: Direct score maximization outperforms planning loss minimization
Carlos Martin, Tuomas Sandholm
Planning at execution time has been shown to dramatically improve performance for agents in both single-agent and multi-agent settings. A well-known family of approaches to plannin…
cs.AI2023
AI planning in the imagination: High-level planning on learned abstract search spaces
Carlos Martin, Tuomas Sandholm
Search and planning algorithms have been a cornerstone of artificial intelligence since the field's inception. Giving reinforcement learning agents the ability to plan during execu…
cs.AI2020★ 2 cited
Efficient exploration of zero-sum stochastic games
Carlos Martin, Tuomas Sandholm
We investigate the increasingly important and common game-solving setting where we do not have an explicit description of the game but only oracle access to it through gameplay, su…