42 citations · 43 across the 7 of their papers we have counts for
7 papers
Beyond Monotonic Progress: Retry-Supervised Value Learning for Robot Imitation
Xinyao Qin, Junjie Lu, Kaixin Wang +7
Human demonstrations for robot imitation learning often contain mistakes and corrective behaviors, such as imprecise grasps, object misalignment, unstable contact, and repeated att…
SEAGym: An Evaluation Environment for Self-Evolving LLM Agents
Congjie Zheng, Chuanyi Xue, Bin Liang +2
Self-evolving LLM-based agents improve mainly by changing their agent harness: the structured execution layer around a base model, including prompts, memory, tools, middleware, run…
UniSteer: Unified Noise Steering for Efficient Human-Guided VLA Adaptation
Junjie Lu, Xinyao Qin, Yuhua Jiang +6
Diffusion-based vision-language-action (VLA) models have emerged as strong priors for robotic manipulation, yet adapting them to real-world distributions remains challenging. In pa…
Efficient Multi-agent Reinforcement Learning by Planning
Qihan Liu, Jianing Ye, Xiaoteng Ma +3
Multi-agent reinforcement learning (MARL) algorithms have accomplished remarkable breakthroughs in solving large-scale decision-making tasks. Nonetheless, most existing MARL algori…
Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox
Qiyue Yin, Tongtong Yu, Shengqi Shen +5
With the breakthrough of AlphaGo, deep reinforcement learning becomes a recognized technique for solving sequential decision-making problems. Despite its reputation, data inefficie…
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization
Tiantian Zhang, Zichuan Lin, Yuxing Wang +7
A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the RL agent's behavior as the environment changes over its lifetime, while m…
Latent-Variable Advantage-Weighted Policy Optimization for Offline RL
Xi Chen, Ali Ghadirzadeh, Tianhe Yu +6
Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions. This setting…