40 citations · 52 across the 10 of their papers we have counts for
10 papers
A Robust and Efficient Multi-Agent Reinforcement Learning Framework for Traffic Signal Control
Sheng-You Huang, Hsiao-Chuan Chang, Yen-Chi Chen +6
Reinforcement Learning (RL) in Traffic Signal Control (TSC) faces significant hurdles in real-world deployment due to limited generalization to dynamic traffic flow variations. Exi…
A Local-Pattern Related Look-Up Table
Chung-Chin Shih, Ting Han Wei, Ti-Rong Wu +1
This paper describes a Relevance-Zone pattern table (RZT) that can be used to replace a traditional transposition table. An RZT stores exact game values for patterns that are disco…
A Novel Approach to Solving Goal-Achieving Problems for Board Games
Chung-Chin Shih, Ti-Rong Wu, Ting Han Wei +1
Goal-achieving problems are puzzles that set up a specific situation with a clear objective. An example that is well-studied is the category of life-and-death (L&D) problems for Go…
Sim-To-Real Transfer for Miniature Autonomous Car Racing
Yeong-Jia Roger Chu, Ting-Han Wei, Jin-Bo Huang +2
Sim-to-real, a term that describes where a model is trained in a simulator then transferred to the real world, is a technique that enables faster deep reinforcement learning (DRL)…
Accelerating and Improving AlphaZero Using Population Based Training
Ti-Rong Wu, Ting-Han Wei, I-Chen Wu
AlphaZero has been very successful in many games. Unfortunately, it still consumes a huge amount of computing resources, the majority of which is spent in self-play. Hyperparameter…
Multiple Policy Value Monte Carlo Tree Search
Li-Cheng Lan, Wei Li, Ting-Han Wei +1
Many of the strongest game playing programs use a combination of Monte Carlo tree search (MCTS) and deep neural networks (DNN), where the DNNs are used as policy or value evaluator…
Towards Combining On-Off-Policy Methods for Real-World Applications
Kai-Chun Hu, Chen-Huan Pi, Ting Han Wei +4
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 lo…
Comparison Training for Computer Chinese Chess
Wen-Jie Tseng, Jr-Chang Chen, I-Chen Wu +1
This paper describes the application of comparison training (CT) for automatic feature weight tuning, with the final objective of improving the evaluation functions used in Chinese…
Multi-Labelled Value Networks for Computer Go
Ti-Rong Wu, I-Chen Wu, Guan-Wun Chen +4
This paper proposes a new approach to a novel value network architecture for the game Go, called a multi-labelled (ML) value network. In the ML value network, different values (win…
Human vs. Computer Go: Review and Prospect
Chang-Shing Lee, Mei-Hui Wang, Shi-Jim Yen +6
The Google DeepMind challenge match in March 2016 was a historic achievement for computer Go development. This article discusses the development of computational intelligence (CI)…