5 citations · 11 across the 8 of their papers we have counts for
8 papers
Exploring Expert Failures Improves LLM Agent Tuning
Li-Cheng Lan, Andrew Bai, Minhao Cheng +2
Large Language Models (LLMs) have shown tremendous potential as agents, excelling at tasks that require multiple rounds of reasoning and interactions. Rejection Sampling Fine-Tunin…
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning
Chia-Cheng Chiang, Li-Cheng Lan, Wei-Fang Sun +3
In this paper, we focus on single-demonstration imitation learning (IL), a practical approach for real-world applications where acquiring multiple expert demonstrations is costly o…
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories
Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh
In this paper, we define, evaluate, and improve the ``relay-generalization'' performance of reinforcement learning (RL) agents on the out-of-distribution ``controllable'' states. I…
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…
How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers
Yuanhao Xiong, Xuanqing Liu, Li-Cheng Lan +3
Many optimizers have been proposed for training deep neural networks, and they often have multiple hyperparameters, which make it tricky to benchmark their performance. In this wor…
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…
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…