Search Results for author:
Found 6 papers, 4 papers with code
Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chess
This paper presents a new approach that integrates deep learning with computational chess, using both the Mixture of Experts (MoE) method and Monte-Carlo Tree Search (MCTS).
Know your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommerman
In combination with Reinforcement Learning, Monte-Carlo Tree Search has shown to outperform human grandmasters in games such as Chess, Shogi and Go with little to no prior domain knowledge.
Representation Matters: The Game of Chess Poses a Challenge to Vision Transformers
While transformers have gained the reputation as the "Swiss army knife of AI", no one has challenged them to master the game of chess, one of the classical AI benchmarks.
Generative Adversarial Neural Cellular Automata
Motivated by the interaction between cells, the recently introduced concept of Neural Cellular Automata shows promising results in a variety of tasks.
Monte-Carlo Graph Search for AlphaZero
The AlphaZero algorithm has been successfully applied in a range of discrete domains, most notably board games.
Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data
Crazyhouse is a game with a higher branching factor than chess and there is only limited data of lower quality available compared to AlphaGo.
