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Found 5 papers, 3 papers with code
Game Solving with Online Fine-Tuning
Solving a game typically means to find the game-theoretic value (outcome given optimal play), and optionally a full strategy to follow in order to achieve that outcome.
MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games
This paper presents MiniZero, a zero-knowledge learning framework that supports four state-of-the-art algorithms, including AlphaZero, MuZero, Gumbel AlphaZero, and Gumbel MuZero.
A Local-Pattern Related Look-Up Table
Experiments also show that the use of an RZT instead of a traditional transposition table significantly reduces the number of searched nodes on two data sets of 7x7 and 19x19 L&D Go problems.
A Novel Approach to Solving Goal-Achieving Problems for Board Games
This paper first proposes a novel RZ-based approach, called the RZ-Based Search (RZS), to solving L&D problems for Go.
AlphaZero-based Proof Cost Network to Aid Game Solving
We train a Proof Cost Network (PCN), where proof cost is a heuristic that estimates the amount of work required to solve problems.
