Search Results for author:
Found 5 papers, 3 papers with code
Amplifying Exploration in Monte-Carlo Tree Search by Focusing on the Unknown
It strategically allocates computational resources to focus on promising segments of the search tree, making it a very attractive search algorithm in large search spaces.
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).
From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?
The gameplay of strategic board games such as chess, Go and Hex is often characterized by combinatorial, relational structures -- capturing distinct interactions and non-local patterns -- and not just images.
OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments
In our work, we extend the Atari Learning Environments, the most-used evaluation framework for deep RL approaches, by introducing OCAtari, that performs resource-efficient extractions of the object-centric states for these games.
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.
