Zhang-Wei Hong

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 47

Top Topics

Exploration Strategy

Reinforcement Learning

Inverse Dynamics

Predictive Control

Top Venues

Publications

Phat Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong, Sertac Karaman, Daniela Rus
Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models. CoRR (2024)
Changling Li, Zhang-Wei Hong, Pulkit Agrawal, Divyansh Garg, Joni Pajarinen
ROER: Regularized Optimal Experience Replay. CoRR (2024)
Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James R. Glass, Akash Srivastava, Pulkit Agrawal
Curiosity-driven Red-teaming for Large Language Models. ICLR (2024)
Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James R. Glass, Akash Srivastava, Pulkit Agrawal
Curiosity-driven Red-teaming for Large Language Models. CoRR (2024)
Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal
Random Latent Exploration for Deep Reinforcement Learning. CoRR (2024)
Yu-Ming Chen, Kuan-Yu Chang, Chien Liu, Tsu-Ching Hsiao, Zhang-Wei Hong, Chun-Yi Lee
Composing Synergistic Macro Actions for Reinforcement Learning Agents. IEEE Trans. Neural Networks Learn. Syst. 35 (5) (2024)
Srinath Mahankali, Chi-Chang Lee, Gabriel B. Margolis, Zhang-Wei Hong, Pulkit Agrawal
Maximizing Quadruped Velocity by Minimizing Energy. ICRA (2024)
Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila R. Fiete
Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building. Trans. Mach. Learn. Res. 2024 (2024)
Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal
TGRL: An Algorithm for Teacher Guided Reinforcement Learning. CoRR (2023)
Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal
TGRL: An Algorithm for Teacher Guided Reinforcement Learning. ICML (2023)
Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal
Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation. ICML (2023)
Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets. NeurIPS (2023)
Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets. CoRR (2023)
Zhang-Wei Hong, Pulkit Agrawal, Remi Tachet des Combes, Romain Laroche
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting. ICLR (2023)
Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie
Model Predictive Control via On-Policy Imitation Learning. L4DC (2023)
Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete
Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity. CoRR (2023)
Charles Jin, Zhang-Wei Hong, Farid Arthaud, Idan Orzech, Martin C. Rinard
Decentralized Inference via Capability Type Structures in Cooperative Multi-Agent Systems. CoRR (2023)
Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal
Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation. CoRR (2023)
Zhang-Wei Hong, Pulkit Agrawal, Rémi Tachet des Combes, Romain Laroche
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting. CoRR (2023)
Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete
Neuro-Inspired Efficient Map Building via Fragmentation and Recall. CoRR (2023)
Zhang-Wei Hong, Ge Yang, Pulkit Agrawal
Bilinear value networks. CoRR (2022)
Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal
Topological Experience Replay. CoRR (2022)
Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie
Model Predictive Control via On-Policy Imitation Learning. CoRR (2022)
Haokuan Luo, Albert Yue, Zhang-Wei Hong, Pulkit Agrawal
Stubborn: A Strong Baseline for Indoor Object Navigation. IROS (2022)
Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal
Redeeming intrinsic rewards via constrained optimization. NeurIPS (2022)
Zhang-Wei Hong, Ge Yang, Pulkit Agrawal
Bi-linear Value Networks for Multi-goal Reinforcement Learning. ICLR (2022)
Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal
Topological Experience Replay. ICLR (2022)
Haokuan Luo, Albert Yue, Zhang-Wei Hong, Pulkit Agrawal
Stubborn: A Strong Baseline for Indoor Object Navigation. CoRR (2022)
Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal
Redeeming Intrinsic Rewards via Constrained Optimization. CoRR (2022)
Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda
Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning. ECML/PKDD (1) (2021)
Chin-Jui Chang, Yu-Wei Chu, Chao-Hsien Ting, Hao-Kang Liu, Zhang-Wei Hong, Chun-Yi Lee
Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture. CoRR (2021)
Chin-Jui Chang, Yu-Wei Chu, Chao-Hsien Ting, Hao-Kang Liu, Zhang-Wei Hong, Chun-Yi Lee
Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture. ICRA (2021)
Po-Han Chiang, Hsuan-Kung Yang, Zhang-Wei Hong, Chun-Yi Lee
Mixture of Step Returns in Bootstrapped DQN. CoRR (2020)
Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda
Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning. CoRR (2020)
Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Chun-Yi Lee
Adversarial Active Exploration for Inverse Dynamics Model Learning. CoRL (2019)
Zhang-Wei Hong, Joni Pajarinen, Jan Peters
Model-based Lookahead Reinforcement Learning. CoRR (2019)
Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Chun-Yi Lee
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. CoRR (2018)
Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu, Chun-Yi Lee
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. NeurIPS (2018)
Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
Adversarial Exploration Strategy for Self-Supervised Imitation Learning. CoRR (2018)
Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Chun-Yi Lee
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. ICLR (Workshop) (2018)
Zhang-Wei Hong, Yu-Ming Chen, Hsuan-Kung Yang, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Brian Hsi-Lin Ho, Chih-Chieh Tu, Tsu-Ching Hsiao, Hsin-Wei Hsiao, Sih-Pin Lai, Yueh-Chuan Chang, Chun-Yi Lee
Virtual-to-Real: Learning to Control in Visual Semantic Segmentation. IJCAI (2018)
Zhang-Wei Hong, Yu-Ming Chen, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Hsuan-Kung Yang, Brian Hsi-Lin Ho, Chih-Chieh Tu, Yueh-Chuan Chang, Tsu-Ching Hsiao, Hsin-Wei Hsiao, Sih-Pin Lai, Chun-Yi Lee
Virtual-to-Real: Learning to Control in Visual Semantic Segmentation. CoRR (2018)
Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
A Deep Policy Inference Q-Network for Multi-Agent Systems. AAMAS (2018)
Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
A Deep Policy Inference Q-Network for Multi-Agent Systems. CoRR (2017)
Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents. IJCAI (2017)
Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents. ICLR (Workshop) (2017)
Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents. CoRR (2017)