1 citations · 3 across the 6 of their papers we have counts for
6 papers
Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
Ruiqi Zhang, Jing Hou, Florian Walter +7
Reinforcement Learning (RL) is a potent tool for sequential decision-making and has achieved performance surpassing human capabilities across many challenging real-world tasks. As…
Explaining an Agent's Future Beliefs through Temporally Decomposing Future Reward Estimators
Mark Towers, Yali Du, Christopher Freeman +1
Future reward estimation is a core component of reinforcement learning agents; i.e., Q-value and state-value functions, predicting an agent's sum of future rewards. Their scalar ou…
Natural Language Reinforcement Learning
Xidong Feng, Ziyu Wan, Mengyue Yang +5
Reinforcement Learning (RL) has shown remarkable abilities in learning policies for decision-making tasks. However, RL is often hindered by issues such as low sample efficiency, la…
TAPE: Leveraging Agent Topology for Cooperative Multi-Agent Policy Gradient
Xingzhou Lou, Junge Zhang, Timothy J. Norman +2
Multi-Agent Policy Gradient (MAPG) has made significant progress in recent years. However, centralized critics in state-of-the-art MAPG methods still face the centralized-decentral…
Cooperation on the Fly: Exploring Language Agents for Ad Hoc Teamwork in the Avalon Game
Zijing Shi, Meng Fang, Shunfeng Zheng +3
Multi-agent collaboration with Large Language Models (LLMs) demonstrates proficiency in basic tasks, yet its efficiency in more complex scenarios remains unexplored. In gaming envi…
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank
Jiarui Jin, Xianyu Chen, Weinan Zhang +5
Learning-to-rank is a core technique in the top-N recommendation task, where an ideal ranker would be a mapping from an item set to an arrangement (a.k.a. permutation). Most existi…