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Found 7 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.
Rethinking Deep Policy Gradients via State-Wise Policy Improvement
Deep policy gradient is one of the major frameworks in reinforcement learning, and it has been shown to improve parameterized policies across various tasks and environments.
Towards Combining On-Off-Policy Methods for Real-World Applications
In this paper, we point out a fundamental property of the objective in reinforcement learning, with which we can reformulate the policy gradient objective into a perceptron-like loss function, removing the need to distinguish between on and off policy training.
