3 citations · 5 across the 7 of their papers we have counts for
7 papers
TTSE: A Two-Track Online Self-Evolution Framework
Ruimin Pei, Yongkang Wu, Shangyi Zheng +5
As Large Language Model (LLM) agents are applied in continuously interactive environments, driving the evolution of their own capabilities becomes a core problem for achieving long…
Exploration by Random Distribution Distillation
Zhirui Fang, Kai Yang, Jian Tao +4
Exploration remains a critical challenge in online reinforcement learning, as an agent must effectively explore unknown environments to achieve high returns. Currently, the main ex…
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning
Yangkun Chen, Kai Yang, Jian Tao +1
Recently, deep Multi-Agent Reinforcement Learning (MARL) has demonstrated its potential to tackle complex cooperative tasks, pushing the boundaries of AI in collaborative environme…
World Models with Hints of Large Language Models for Goal Achieving
Zeyuan Liu, Ziyu Huan, Xiyao Wang +5
Reinforcement learning struggles in the face of long-horizon tasks and sparse goals due to the difficulty in manual reward specification. While existing methods address this by add…
Exploration and Anti-Exploration with Distributional Random Network Distillation
Kai Yang, Jian Tao, Jiafei Lyu +1
Exploration remains a critical issue in deep reinforcement learning for an agent to attain high returns in unknown environments. Although the prevailing exploration Random Network…
Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model
Kai Yang, Jian Tao, Jiafei Lyu +6
Using reinforcement learning with human feedback (RLHF) has shown significant promise in fine-tuning diffusion models. Previous methods start by training a reward model that aligns…
Multi-agent Exploration with Sub-state Entropy Estimation
Jian Tao, Yang Zhang, Yangkun Chen +1
Researchers have integrated exploration techniques into multi-agent reinforcement learning (MARL) algorithms, drawing on their remarkable success in deep reinforcement learning. No…