no code implementations • ICML 2020 • Geon-Hyeong Kim, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim
The estimated future likelihoods form the core of our new low-variance gradient estimator.
2 code implementations • 28 Feb 2022 • Geon-Hyeong Kim, Jongmin Lee, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim
We consider the problem of learning from observation (LfO), in which the agent aims to mimic the expert's behavior from the state-only demonstrations by experts.
no code implementations • NeurIPS 2021 • HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
Multi-View Representation Learning (MVRL) aims to discover a shared representation of observations from different views with the complex underlying correlation.
no code implementations • ICLR 2022 • Geon-Hyeong Kim, Seokin Seo, Jongmin Lee, Wonseok Jeon, HyeongJoo Hwang, Hongseok Yang, Kee-Eung Kim
We consider offline imitation learning (IL), which aims to mimic the expert's behavior from its demonstration without further interaction with the environment.
2 code implementations • NeurIPS 2020 • HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
Grounded in information theory, we cast the simultaneous learning of domain-invariant and domain-specific representations as a joint objective of multiple information constraints, which does not require adversarial training or gradient reversal layers.
no code implementations • NeurIPS 2018 • Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart, Kee-Eung Kim
In this paper, we present CC-POMCP (Cost-Constrained POMCP), an online MCTS algorithm for large CPOMDPs that leverages the optimization of LP-induced parameters and only requires a black-box simulator of the environment.