Zhang-Wei Hong

According to our database, Zhang-Wei Hong authored at least 36 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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

CoRR, February, 2025

2024

Composing Synergistic Macro Actions for Reinforcement Learning Agents.

Yu-Ming Chen, Kuan-Yu Chang, Chien Liu, Tsu-Ching Hsiao, Zhang-Wei Hong, Chun-Yi Lee

IEEE Trans. Neural Networks Learn. Syst., May, 2024

Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building.

Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila R. Fiete

Trans. Mach. Learn. Res., 2024

Embodied Red Teaming for Auditing Robotic Foundation Models.

Sathwik Karnik, Zhang-Wei Hong, Nishant Abhangi, Yen-Chen Lin, Tsun-Hsuan Wang, Pulkit Agrawal

CoRR, 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, Ila Fiete

CoRR, 2024

ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization.

Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano, Pulkit Agrawal

CoRR, 2024

ROER: Regularized Optimal Experience Replay.

Changling Li, Zhang-Wei Hong, Pulkit Agrawal, Divyansh Garg, Joni Pajarinen

RLJ, 2024

Going Beyond Heuristics by Imposing Policy Improvement as a Constraint.

Chi-Chang Lee, Zhang-Wei Hong, Pulkit Agrawal

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models.

Phat Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong, Sertac Karaman, Daniela Rus

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024

Maximizing Quadruped Velocity by Minimizing Energy.

Srinath Mahankali, Chi-Chang Lee, Gabriel B. Margolis, Zhang-Wei Hong, Pulkit Agrawal

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Random Latent Exploration for Deep Reinforcement Learning.

Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity.

Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete

CoRR, 2023

Neuro-Inspired Efficient Map Building via Fragmentation and Recall.

Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete

CoRR, 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

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, Pulkit Agrawal

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Model Predictive Control via On-Policy Imitation Learning.

Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie

Proceedings of the Learning for Dynamics and Control Conference, 2023

TGRL: An Algorithm for Teacher Guided Reinforcement Learning.

Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal

Proceedings of the International Conference on Machine Learning, 2023

Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation.

Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal

Proceedings of the International Conference on Machine Learning, 2023

Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting.

Zhang-Wei Hong, Pulkit Agrawal, Remi Tachet des Combes, Romain Laroche

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Bilinear value networks.

Zhang-Wei Hong, Ge Yang, Pulkit Agrawal

CoRR, 2022

Redeeming intrinsic rewards via constrained optimization.

Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Stubborn: A Strong Baseline for Indoor Object Navigation.

Haokuan Luo, Albert Yue, Zhang-Wei Hong, Pulkit Agrawal

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Bi-linear Value Networks for Multi-goal Reinforcement Learning.

Zhang-Wei Hong, Ge Yang, Pulkit Agrawal

Proceedings of the Tenth International Conference on Learning Representations, 2022

Topological Experience Replay.

Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning.

Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

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

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020

Mixture of Step Returns in Bootstrapped DQN.

Po-Han Chiang, Hsuan-Kung Yang, Zhang-Wei Hong, Chun-Yi Lee

CoRR, 2020

2019

Model-based Lookahead Reinforcement Learning.

Zhang-Wei Hong, Joni Pajarinen, Jan Peters

CoRR, 2019

Adversarial Active Exploration for Inverse Dynamics Model Learning.

Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Chun-Yi Lee

Proceedings of the 3rd Annual Conference on Robot Learning, 2019

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

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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Virtual-to-Real: Learning to Control in Visual Semantic Segmentation.

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

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Diversity-Driven Exploration Strategy for Deep Reinforcement Learning.

Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Chun-Yi Lee

Proceedings of the 6th International Conference on Learning Representations, 2018

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

Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

2017

Tactics of Adversarial Attack on Deep Reinforcement Learning Agents.

Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017