Tactics of Adversarial Attack on Deep Reinforcement Learning Agents. - Yen-Chen Lin; Zhang-Wei Hong; Yuan-Hong Liao; Meng-Li Shih; Ming-Yu Liu 001; Min Sun 001 (IJCAI, 2017)

A Deep Policy Inference Q-Network for Multi-Agent Systems. - Zhang-Wei Hong; Shih-Yang Su; Tzu-Yun Shann; Yi-Hsiang Chang; Chun-Yi Lee (AAMAS, 2018)

Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. - Zhang-Wei Hong; Tzu-Yun Shann; Shih-Yang Su; Yi-Hsiang Chang; Chun-Yi Lee (ICLR (Workshop), 2018)

Virtual-to-Real: Learning to Control in Visual Semantic Segmentation. - Zhang-Wei Hong; Yu-Ming Chen 002; 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; et al. (IJCAI, 2018)

Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. - Zhang-Wei Hong; Tzu-Yun Shann; Shih-Yang Su; Yi-Hsiang Chang; Tsu-Jui Fu; Chun-Yi Lee (NeurIPS, 2018)

Adversarial Exploration Strategy for Self-Supervised Imitation Learning. - Zhang-Wei Hong; Tsu-Jui Fu; Tzu-Yun Shann; Yi-Hsiang Chang; Chun-Yi Lee (CoRR, 2018)

Adversarial Active Exploration for Inverse Dynamics Model Learning. - Zhang-Wei Hong; Tsu-Jui Fu; Tzu-Yun Shann; Chun-Yi Lee (CoRL, 2019)

Model-based Lookahead Reinforcement Learning. - Zhang-Wei Hong; Joni Pajarinen; Jan Peters 001 (CoRR, 2019)

Mixture of Step Returns in Bootstrapped DQN. - Po-Han Chiang; Hsuan-Kung Yang; Zhang-Wei Hong; Chun-Yi Lee (CoRR, 2020)

Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture. - Chin-Jui Chang; Yu-Wei Chu; Chao-Hsien Ting; Hao-Kang Liu; Zhang-Wei Hong; Chun-Yi Lee (ICRA, 2021)

Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning. - Zhang-Wei Hong; Prabhat Nagarajan; Guilherme Maeda (ECML/PKDD (1), 2021)

Topological Experience Replay. - Zhang-Wei Hong; Tao Chen 046; Yen-Chen Lin; Joni Pajarinen; Pulkit Agrawal 001 (ICLR, 2022)

Bi-linear Value Networks for Multi-goal Reinforcement Learning. - Zhang-Wei Hong; Ge Yang 003; Pulkit Agrawal 001 (ICLR, 2022)

Stubborn: A Strong Baseline for Indoor Object Navigation. - Haokuan Luo; Albert Yue; Zhang-Wei Hong; Pulkit Agrawal 001 (IROS, 2022)

Redeeming intrinsic rewards via constrained optimization. - Eric Chen; Zhang-Wei Hong; Joni Pajarinen; Pulkit Agrawal 001 (NeurIPS, 2022)

Bilinear value networks. - Zhang-Wei Hong; Ge Yang 003; Pulkit Agrawal 001 (CoRR, 2022)

Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting. - Zhang-Wei Hong; Pulkit Agrawal 001; Remi Tachet des Combes; Romain Laroche (ICLR, 2023)

Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation. - Zechu Li; Tao Chen 046; Zhang-Wei Hong; Anurag Ajay; Pulkit Agrawal 001 (ICML, 2023)

TGRL: An Algorithm for Teacher Guided Reinforcement Learning. - Idan Shenfeld; Zhang-Wei Hong; Aviv Tamar; Pulkit Agrawal 001 (ICML, 2023)

Model Predictive Control via On-Policy Imitation Learning. - Kwangjun Ahn; Zakaria Mhammedi; Horia Mania; Zhang-Wei Hong; Ali Jadbabaie (L4DC, 2023)

Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets. - Zhang-Wei Hong; Aviral Kumar; Sathwik Karnik; Abhishek Bhandwaldar; Akash Srivastava; Joni Pajarinen; Romain Laroche; Abhishek Gupta 004; Pulkit Agrawal 001 (NeurIPS, 2023)

Decentralized Inference via Capability Type Structures in Cooperative Multi-Agent Systems. - Charles Jin; Zhang-Wei Hong; Farid Arthaud; Idan Orzech; Martin C. Rinard (CoRR, 2023)

Neuro-Inspired Efficient Map Building via Fragmentation and Recall. - Jaedong Hwang; Zhang-Wei Hong; Eric Chen; Akhilan Boopathy; Pulkit Agrawal 001; Ila Fiete (CoRR, 2023)

Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity. - Jaedong Hwang; Zhang-Wei Hong; Eric Chen; Akhilan Boopathy; Pulkit Agrawal 001; Ila Fiete (CoRR, 2023)

Curiosity-driven Red-teaming for Large Language Models. - Zhang-Wei Hong; Idan Shenfeld; Tsun-Hsuan Wang; Yung-Sung Chuang; Aldo Pareja; James R. Glass; Akash Srivastava; Pulkit Agrawal 001 (ICLR, 2024)

Random Latent Exploration for Deep Reinforcement Learning. - Srinath Mahankali; Zhang-Wei Hong; Ayush Sekhari; Alexander Rakhlin; Pulkit Agrawal 001 (ICML, 2024)

Maximizing Quadruped Velocity by Minimizing Energy. - Srinath Mahankali; Chi-Chang Lee; Gabriel B. Margolis; Zhang-Wei Hong; Pulkit Agrawal 001 (ICRA, 2024)

Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models. - Phat Nguyen; Tsun-Hsuan Wang; Zhang-Wei Hong; Sertac Karaman; Daniela Rus (IROS, 2024)

Going Beyond Heuristics by Imposing Policy Improvement as a Constraint. - Chi-Chang Lee; Zhang-Wei Hong; Pulkit Agrawal 001 (NeurIPS, 2024)

ROER: Regularized Optimal Experience Replay. - Changling Li; Zhang-Wei Hong; Pulkit Agrawal 001; Divyansh Garg; Joni Pajarinen (RLJ, 2024)

ImageNet-RIB Benchmark: Large Pre-Training Datasets Don't Guarantee Robustness after Fine-Tuning. - Jaedong Hwang; Brian Cheung; Zhang-Wei Hong; Akhilan Boopathy; Pulkit Agrawal 001; Ila Fiete (CoRR, 2024)

Embodied Red Teaming for Auditing Robotic Foundation Models. - Sathwik Karnik; Zhang-Wei Hong; Nishant Abhangi; Yen-Chen Lin; Tsun-Hsuan Wang; Pulkit Agrawal 001 (CoRR, 2024)

Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building. - Jaedong Hwang; Zhang-Wei Hong; Eric Chen; Akhilan Boopathy; Pulkit Agrawal 001; Ila R. Fiete (Trans. Mach. Learn. Res., 2024)

Composing Synergistic Macro Actions for Reinforcement Learning Agents. - Yu-Ming Chen 002; Kuan-Yu Chang; Chien Liu; Tsu-Ching Hsiao; Zhang-Wei Hong; Chun-Yi Lee (IEEE Trans. Neural Networks Learn. Syst., 2024)

ReGen: Generative Robot Simulation via Inverse Design. - Phat Tan Nguyen; Tsun-Hsuan Wang; Zhang-Wei Hong; Erfan Aasi; Andrew Silva; Guy Rosman; Sertac Karaman; Daniela Rus (ICLR, 2025)

ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization. - Chen Bo Calvin Zhang; Zhang-Wei Hong; Aldo Pacchiano; Pulkit Agrawal 001 (ICLR, 2025)

A distributional reinforcement learning model for optimal glucose control after cardiac surgery. - Jacob Desman; Zhang-Wei Hong; Moein Sabounchi; Ashwin S. Sawant; Jaskirat Gill; Ana C. Costa; Gagan Kumar; Rajeev Sharma; Arpeta Gupta; Paul McCarthy; Veena Nandwani; Doug Powell; et al. (npj Digit. Medicine, 2025)

Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search. - Maohao Shen; Guangtao Zeng; Zhenting Qi; Zhang-Wei Hong; Zhenfang Chen; Wei Lu; Gregory W. Wornell; Subhro Das; David Cox; Chuang Gan 001 (CoRR, 2025)

RL Tango: Reinforcing Generator and Verifier Together for Language Reasoning. - Kaiwen Zha; Zhengqi Gao; Maohao Shen; Zhang-Wei Hong; Duane S. Boning; Dina Katabi (CoRR, 2025)

Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering. - Guangtao Zeng; Maohao Shen; Delin Chen; Zhenting Qi; Subhro Das; Dan Gutfreund; David Cox; Gregory W. Wornell; Wei Lu; Zhang-Wei Hong; Chuang Gan 001 (CoRR, 2025)