5 citations · 8 across the 3 of their papers we have counts for
4 papers
Are AlphaZero-like Agents Robust to Adversarial Perturbations?
Li-Cheng Lan, Huan Zhang, Ti-Rong Wu +3
The success of AlphaZero (AZ) has demonstrated that neural-network-based Go AIs can surpass human performance by a large margin. Given that the state space of Go is extremely large…
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…
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…
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…