1 code implementation • 1 Feb 2024 • Chia-Cheng Chiang, Li-Cheng Lan, Wei-Fang Sun, Chien Feng, Cho-Jui Hsieh, Chun-Yi Lee
In this paper, we focus on single-demonstration imitation learning (IL), a practical approach for real-world applications where obtaining numerous expert demonstrations is costly or infeasible.
no code implementations • 26 Apr 2023 • Li-Cheng Lan, huan zhang, Cho-Jui Hsieh
With extensive experimental evaluation, we show the prevalence of \emph{generalization failure} on controllable states from stranger agents.
1 code implementation • 7 Nov 2022 • Li-Cheng Lan, huan zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, Cho-Jui Hsieh
Given that the state space of Go is extremely large and a human player can play the game from any legal state, we ask whether adversarial states exist for Go AIs that may lead them to play surprisingly wrong actions.
no code implementations • ICLR 2022 • Yuanhao Xiong, Li-Cheng Lan, Xiangning Chen, Ruochen Wang, Cho-Jui Hsieh
By constructing a directed graph for the underlying neural network of the target problem, GNS encodes current dynamics with a graph message passing network and trains an agent to control the learning rate accordingly via reinforcement learning.
no code implementations • 14 Dec 2020 • Li-Cheng Lan, Meng-Yu Tsai, Ti-Rong Wu, I-Chen Wu, Cho-Jui Hsieh
This implies that a significant amount of resources can be saved if we are able to stop the searching earlier when we are confident with the current searching result.
no code implementations • 19 Oct 2020 • Yuanhao Xiong, Xuanqing Liu, Li-Cheng Lan, Yang You, Si Si, Cho-Jui Hsieh
For end-to-end efficiency, unlike previous work that assumes random hyperparameter tuning, which over-emphasizes the tuning time, we propose to evaluate with a bandit hyperparameter tuning strategy.
2 code implementations • 31 May 2019 • Li-Cheng Lan, Wei Li, Ting-Han Wei, I-Chen Wu
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 evaluators.
no code implementations • 30 May 2017 • Ti-Rong Wu, I-Chen Wu, Guan-Wun Chen, Ting-Han Wei, Tung-Yi Lai, Hung-Chun Wu, Li-Cheng Lan
First, the MSE of the ML value network is generally lower than the value network alone.