2 citations · 5 across the 11 of their papers we have counts for
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cs.AI2020
Minimax Strikes Back
Quentin Cohen-Solal, Tristan Cazenave
Deep Reinforcement Learning reaches a superhuman level of play in many complete information games. The state of the art algorithm for learning with zero knowledge is AlphaZero. We…
cs.AI2020★ 2 cited
Learning to Play Two-Player Perfect-Information Games without Knowledge
Quentin Cohen-Solal
In this paper, several techniques for learning game state evaluation functions by reinforcement are proposed. The first is a generalization of tree bootstrapping (tree learning): i…
cs.AI2020★ 2 cited
Tractable Fragments of Temporal Sequences of Topological Information
Quentin Cohen-Solal
In this paper, we focus on qualitative temporal sequences of topological information. We firstly consider the context of topological temporal sequences of length greater than 3 des…