10 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…
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…
Learning Human-Like RL Agents Through Trajectory Optimization With Action Quantization
Jian-Ting Guo, Yu-Cheng Chen, Ping-Chun Hsieh +4
Human-like agents have long been one of the goals in pursuing artificial intelligence. Although reinforcement learning (RL) has achieved superhuman performance in many domains, rel…
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…
Bridging Local and Global Knowledge via Transformer in Board Games
Yan-Ru Ju, Tai-Lin Wu, Chung-Chin Shih +1
Although AlphaZero has achieved superhuman performance in board games, recent studies reveal its limitations in handling scenarios requiring a comprehensive understanding of the en…
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…