This is the page for the Monte Carlo Search course of the master IASD.
Slides of the course: MonteCarlo.pdf.
Breakthrough: Breakthrough.ipynb.
Nested LeftMove: Nested-LeftMove.ipynb.
Nested Expression Discovery: ExpressionDiscovery.ipynb.
NRPA LeftMove: NRPA-LeftMove.ipynb.
"A Survey of Monte Carlo Tree Search Methods", Cameron Browne et al. IEEE TCIAIG 2012. survey.pdf.
"Monte-Carlo tree search and rapid action value estimation in computer Go", Sylvain Gelly, David Silver. Artificial Intelligence, 2011 rave.pdf
"Generalized Rapid Action Value Estimation", Tristan Cazenave. IJCAI 2015, pp. 754-760. grave.pdf
"Sequential Halving Using Scores", Nicolas Fabiano, Tristan Cazenave. Advances in Computer Games 2021. SHUSS.pdf
"Nested Monte-Carlo Search", T. Cazenave. IJCAI 2009, pp. 456-461, Pasadena, July 2009. nested.pdf
"Nested Monte Carlo Search for Two-player Games", Tristan Cazenave, Abdallah Saffidine, Michael Schofield, Michael Thielscher. AAAI 2016, pp. 687-693. 12134-55519-1-PB.pdf
"Nested Rollout Policy Adaptation for Monte Carlo Tree Search", Christopher Rosin. IJCAI 2011. nrpa.pdf
"Playout Policy Adaptation with Move Features", Tristan Cazenave. Theoretical Computer Science, Vol. 644, pp. 43-52, 2016. ppatcs.pdf
"A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play", David Silver et al. Science 2018. alphazero.pdf
"Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model", Julian Schrittwieser et al. 2019. muzero.pdf
"Accelerating Self-Play Learning in Go", David J. Wu. AAAI RLG 2020. accelerating.pdf
"Polygames: Improved Zero Learning", Tristan Cazenave et al. ICGA Journal, Vol. 42 (4), pp. 244-256, December 2020. Polygames.pdf
"Minimax Strikes Back", Quentin Cohen-Solal, Tristan Cazenave. AAMAS 2023. MinimaxStrikesBack_AAMAS.pdf, MinimaxStrikesBackSupplementaries.pdf