1 citations · 1 across the 9 of their papers we have counts for
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Task Inference Beyond Least Squares in Behavioral Foundation Models
Kuan-Hsun Tu, Chien-Sheng Chiang, Hsin-Wei Chen +2
Behavioral Foundation Models (BFMs) aim to solve a wide range of downstream tasks without test-time policy learning by inferring a task vector from the reward function. While effic…
Language-Critique Imitation Learning from Suboptimal Demonstrations
Chih-Han Yang, Dai-Jie Wu, Yun-Ping Huang +3
Prior work on imitation learning from suboptimal demonstrations typically relies on compressed supervision signals such as confidence estimates, discriminator scores, or importance…
Plan2Cleanse: Test-Time Backdoor Defense via Monte-Carlo Planning in Deep Reinforcement Learning
Sze-Ann Chen, Zhi-Yi Chin, Kui-Yuan Chen +2
Ensuring the security of reinforcement learning (RL) models is critical, particularly when they are trained by third parties and deployed in real-world systems. Attackers can impla…
DDOT: A Derivative-directed Dual-decoder Ordinary Differential Equation Transformer for Dynamic System Modeling
Yang Chang, Kuang-Da Wang, Ping-Chun Hsieh +2
Uncovering the underlying ordinary differential equations (ODEs) that govern dynamic systems is crucial for advancing our understanding of complex phenomena. Traditional symbolic r…
Efficient Action-Constrained Reinforcement Learning via Acceptance-Rejection Method and Augmented MDPs
Wei Hung, Shao-Hua Sun, Ping-Chun Hsieh
Action-constrained reinforcement learning (ACRL) is a generic framework for learning control policies with zero action constraint violation, which is required by various safety-cri…
Diffusion-Reward Adversarial Imitation Learning
Chun-Mao Lai, Hsiang-Chun Wang, Ping-Chun Hsieh +3
Imitation learning aims to learn a policy from observing expert demonstrations without access to reward signals from environments. Generative adversarial imitation learning (GAIL)…