14 citations · 52 across the 23 of their papers we have counts for
6 papers · 1 filter
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…
Mobile Networks for Computer Go
Tristan Cazenave
The architecture of the neural networks used in Deep Reinforcement Learning programs such as Alpha Zero or Polygames has been shown to have a great impact on the performances of th…
Monte Carlo Inverse Folding
Tristan Cazenave, Thomas Fournier
The RNA Inverse Folding problem comes from computational biology. The goal is to find a molecule that has a given folding. It is important for scientific fields such as bioengineer…
Generalized Nested Rollout Policy Adaptation
Tristan Cazenave
Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm for single player games. In this paper we propose to generalize NRPA with a temperature and a bias and to…
Polygames: Improved Zero Learning
Tristan Cazenave, Yen-Chi Chen, Guan-Wei Chen +21
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games. It can be improved using a fully convolutional structure (no fully connec…
Monte Carlo Game Solver
Tristan Cazenave
We present a general algorithm to order moves so as to speedup exact game solvers. It uses online learning of playout policies and Monte Carlo Tree Search. The learned policy and t…