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Zhang-Wei Hong
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2020 – today
- 2024
- [j3]Changling Li, Zhang-Wei Hong, Pulkit Agrawal, Divyansh Garg, Joni Pajarinen:
ROER: Regularized Optimal Experience Replay. RLJ 4: 1598-1618 (2024) - [j2]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) - [j1]Yu-Ming Chen
, Kuan-Yu Chang
Composing Synergistic Macro Actions for Reinforcement Learning Agents. IEEE Trans. Neural Networks Learn. Syst. 35(5): 7251-7258 (2024) - [c21]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 - [c20]Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal:
Random Latent Exploration for Deep Reinforcement Learning. ICML 2024 - [c19]Srinath Mahankali, Chi-Chang Lee, Gabriel B. Margolis, Zhang-Wei Hong, Pulkit Agrawal:
Maximizing Quadruped Velocity by Minimizing Energy. ICRA 2024: 11467-11473 - [i25]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 abs/2402.19464 (2024) - [i24]Phat Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong, Sertac Karaman, Daniela Rus:
Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models. CoRR abs/2406.04300 (2024) - [i23]Changling Li, Zhang-Wei Hong, Pulkit Agrawal, Divyansh Garg, Joni Pajarinen:
ROER: Regularized Optimal Experience Replay. CoRR abs/2407.03995 (2024) - [i22]Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal:
Random Latent Exploration for Deep Reinforcement Learning. CoRR abs/2407.13755 (2024) - 2023
- [c18]Zhang-Wei Hong, Pulkit Agrawal, Remi Tachet des Combes, Romain Laroche:
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting. ICLR 2023 - [c17]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: 19440-19459 - [c16]Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal:
TGRL: An Algorithm for Teacher Guided Reinforcement Learning. ICML 2023: 31077-31093 - [c15]Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie:
Model Predictive Control via On-Policy Imitation Learning. L4DC 2023: 1493-1505 - [c14]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 - [i21]Charles Jin, Zhang-Wei Hong, Farid Arthaud, Idan Orzech, Martin C. Rinard:
Decentralized Inference via Capability Type Structures in Cooperative Multi-Agent Systems. CoRR abs/2304.13957 (2023) - [i20]Zhang-Wei Hong, Pulkit Agrawal, Rémi Tachet des Combes, Romain Laroche:
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting. CoRR abs/2306.13085 (2023) - [i19]Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal:
TGRL: An Algorithm for Teacher Guided Reinforcement Learning. CoRR abs/2307.03186 (2023) - [i18]Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete:
Neuro-Inspired Efficient Map Building via Fragmentation and Recall. CoRR abs/2307.05793 (2023) - [i17]Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal:
Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation. CoRR abs/2307.12983 (2023) - [i16]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 abs/2310.04413 (2023) - [i15]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 abs/2310.17537 (2023) - 2022
- [c13]Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal:
Topological Experience Replay. ICLR 2022 - [c12]Zhang-Wei Hong, Ge Yang, Pulkit Agrawal:
Bi-linear Value Networks for Multi-goal Reinforcement Learning. ICLR 2022 - [c11]Haokuan Luo, Albert Yue, Zhang-Wei Hong, Pulkit Agrawal:
Stubborn: A Strong Baseline for Indoor Object Navigation. IROS 2022: 3287-3293 - [c10]Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal:
Redeeming intrinsic rewards via constrained optimization. NeurIPS 2022 - [i14]Haokuan Luo, Albert Yue, Zhang-Wei Hong, Pulkit Agrawal:
Stubborn: A Strong Baseline for Indoor Object Navigation. CoRR abs/2203.07359 (2022) - [i13]Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal:
Topological Experience Replay. CoRR abs/2203.15845 (2022) - [i12]Zhang-Wei Hong, Ge Yang, Pulkit Agrawal:
Bilinear value networks. CoRR abs/2204.13695 (2022) - [i11]Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie:
Model Predictive Control via On-Policy Imitation Learning. CoRR abs/2210.09206 (2022) - [i10]Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal:
Redeeming Intrinsic Rewards via Constrained Optimization. CoRR abs/2211.07627 (2022) - 2021
- [c9]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: 4762-4768 - [c8]Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda:
Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning. ECML/PKDD (1) 2021: 87-103 - [i9]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 abs/2105.14471 (2021) - 2020
- [i8]Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda:
Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning. CoRR abs/2002.00149 (2020) - [i7]Po-Han Chiang, Hsuan-Kung Yang, Zhang-Wei Hong, Chun-Yi Lee:
Mixture of Step Returns in Bootstrapped DQN. CoRR abs/2007.08229 (2020)
2010 – 2019
- 2019
- [c7]Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Chun-Yi Lee
Adversarial Active Exploration for Inverse Dynamics Model Learning. CoRL 2019: 552-565 - [i6]Zhang-Wei Hong, Joni Pajarinen, Jan Peters:
Model-based Lookahead Reinforcement Learning. CoRR abs/1908.06012 (2019) - 2018
- [c6]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: 1388-1396 - [c5]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 - [c4]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: 4912-4920 - [c3]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: 10510-10521 - [i5]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 abs/1802.00285 (2018) - [i4]Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Chun-Yi Lee:
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. CoRR abs/1802.04564 (2018) - [i3]Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee:
Adversarial Exploration Strategy for Self-Supervised Imitation Learning. CoRR abs/1806.10019 (2018) - 2017
- [c2]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 - [c1]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: 3756-3762 - [i2]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 abs/1703.06748 (2017) - [i1]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 abs/1712.07893 (2017)
Coauthor Index
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