6 papers
MAPLE: Multi-State Aggregated Policy Evaluation for AlphaZero in Imperfect-Information Games
Qian-Rong Li, Hung Guei, I-Chen Wu +1
Imperfect-information games (IIGs) are challenging, as players must make decisions without fully observing the true game state. While AlphaZero has achieved remarkable success in p…
Evaluating Game Difficulty in Tetris Block Puzzle
Chun-Jui Wang, Jian-Ting Guo, Hung Guei +3
Tetris Block Puzzle is a single player stochastic puzzle in which a player places blocks on an 8 x 8 grid to complete lines; its popular variants have amassed tens of millions of d…
A Study of Solving Life-and-Death Problems in Go Using Relevance-Zone Based Solvers
Chung-Chin Shih, Ti-Rong Wu, Ting Han Wei +3
This paper analyzes the behavior of solving Life-and-Death (L&D) problems in the game of Go using current state-of-the-art computer Go solvers with two techniques: the Relevance-Zo…
Relevance-Zone Reduction in Game Solving
Chi-Huang Lin, Ting Han Wei, Chun-Jui Wang +5
Game solving aims to find the optimal strategies for all players and determine the theoretical outcome of a game. However, due to the exponential growth of game trees, many games r…
Demystifying MuZero Planning: Interpreting the Learned Model
Hung Guei, Yan-Ru Ju, Wei-Yu Chen +1
MuZero has achieved superhuman performance in various games by using a dynamics network to predict the environment dynamics for planning, without relying on simulators. However, th…
OptionZero: Planning with Learned Options
Po-Wei Huang, Pei-Chiun Peng, Hung Guei +1
Planning with options -- a sequence of primitive actions -- has been shown effective in reinforcement learning within complex environments. Previous studies have focused on plannin…