4 papers
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…