5 citations · 10 across the 6 of their papers we have counts for
6 papers · 1 filter
Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search
Li-Cheng Lan, Meng-Yu Tsai, Ti-Rong Wu +2
Monte Carlo tree search (MCTS) has achieved state-of-the-art results in many domains such as Go and Atari games when combining with deep neural networks (DNNs). When more simulatio…
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…
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…