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October 29, 2024

(This blog is mainly translated from Chinese version blog by GPT-4. Refer to Chinese version if you understand chinese! For more details, see: https://openreview.net/forum?id=tb1MlJCY5g)

October 18, 2023

This is a collection of recent papers submitted to ICLR’24, which are focusing on building autonomous agent. These papers have been integrated to this github repo, which is in active maintainance. Feel free to star/follow the repo.

September 22, 2023

This blog introduces our work that will appear in NeurIPS 2023. We focus on using natural language to instruct a robot to complete tasks and propose a practical algorithm.

Short description of portfolio item number 1

Short description of portfolio item number 2

• Rong-Jun Qin, Jing-Cheng Pang and Yang Yu. Improving Fictitious Play Reinforcement Learning with Expanding Models. CoRR abs/1907.01077, 2019.

• Shengyi Jiang, Jing-Cheng Pang and Yang Yu. Offline imitation learning with a misspecified simulator. In: NeurIPS, 2020.

• Xu-Hui Liu*, Zhenghai Xue*, Jing-Cheng Pang, Shengyi Jiang, Feng Xu and Yang Yu. Regret Minimization Experience Replay in Off-Policy Reinforcement Learning. In: NeurIPS, 2021.

• Jing-Cheng Pang, Tian Xu, Shengyi Jiang, Yu-Ren Liu and Yang Yu. Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization. Submitted to IEEE Transactions on Neural Networks and Learning Systems (TNNLS).

• Jing-Cheng Pang*, Si-Hang Yang*, Xiong-Hui Chen, Xinyu Yang, Yang Yu, Mas Ma, Ziqi Guo, Howard Yang and Bill Huang. Object-Oriented Option Framework for Robotics Manipulation in Clutter. In: IROS (Oral), 2023.

• Jing-Cheng Pang*, Xinyu Yang*, Si-Hang Yang, Xiong-Hui Chen and Yang Yu. Natural Language Instruction-following with Task-related Language Development and Translation. In: NeurIPS, 2023.

• Chengxing Jia*, Fuxiang Zhang*, Tian Xu, Jing-Cheng Pang, Zongzhang Zhang and Yang Yu. Model Gradient: Unified Model and Policy Learning in Model-based Reinforcement Learning. Frontiers of Computer Science, 2024.

• Jing-Cheng Pang*, Pengyuan Wang*, Nan Tang, Kaiyuan Li, Xionghui Chen, Jiacheng Xu, Zongzhang Zhang and Yang Yu. Language Model Self-improvement by Reinforcement Learning Contemplation. In: DAI (Poster Paper Track), 2023.

• Jing-Cheng Pang*, Pengyuan Wang*, Kaiyuan Li, Xiong-Hui Chen, Jiacheng Xu, ZongZhang Zhang and Yang Yu. Language Model Self-improvement by Reinforcement Learning Contemplation. In: ICLR, 2024.

• Jing-Cheng Pang*, Heng-Bo Fan*, Pengyuan Wang*, Jia-Hao Xiao*, Nan Tang, Si-Hang Yang, Chengxing Jia, Sheng-Jun Huang and Yang Yu. Empowering Language Models with Active Inquiry for Deeper Understanding. CoRR abs/2402.03719, 2024.

• Jing-Cheng Pang, Kaiyuan Li, Pengyuan Wang, Xiong-Hui Chen, Jiacheng Xu, ZongZhang Zhang and Yang Yu. Language Model Self-improvement by Reinforcement Learning Contemplation without External Supervision. Submitted to Journal of Artificial Intelligence Research (JAIR).

• Jing-Cheng Pang, Si-Hang Yang, Kaiyuan Li, Jiaji Zhang, Xiong-Hui Chen, Nan Tang and Yang Yu. Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts. In: NeurIPS, 2024.

• Yuting Tang*, Xin-Qiang Cai*, Jing-Cheng Pang, Qiyu Wu, Yao-Xiang Ding and Masashi Sugiyama. Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning. CoRR abs/2410.20176, 2024.

• Zhilong Zhang, Ruifeng Chen, Junyin Ye, Yihao Sun, Pengyuan Wang, Jing-Cheng Pang, Kaiyuan Li, Tianshuo Liu, Haoxin Lin, Yang Yu, Zhi-Hua Zhou. WHALE: Towards Generalizable and Scalable World Models for Embodied Decision-making. CoRR abs/2411.05619, 2024.

• Peng-Yuan Wang*, Jing-Cheng Pang*, Chen-Yang Wang*, Xu-Hui Liu, Tian-Shuo Liu, Si-Hang Yang, Hong Qian, Yang Yu. In-context Learning from Language Models can Improve Embodied Instruction-following. In: AAMAS (Oral), 2025.

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Undergraduate course, University 1, Department, 2014

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