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Found 9 papers, 5 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.
Are AlphaZero-like Agents Robust to Adversarial Perturbations?
Given that the state space of Go is extremely large and a human player can play the game from any legal state, we ask whether adversarial states exist for Go AIs that may lead them to play surprisingly wrong actions.
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.
Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search
This implies that a significant amount of resources can be saved if we are able to stop the searching earlier when we are confident with the current searching result.
Accelerating and Improving AlphaZero Using Population Based Training
This is compared to a saturated non-PBT agent, which achieves a win rate of 47% against ELF OpenGo under the same circumstances.
Multi-Labelled Value Networks for Computer Go
First, the MSE of the ML value network is generally lower than the value network alone.
