14 citations · 16 across the 5 of their papers we have counts for
6 papers
Improving LLM Collaboration via Multi-Agent Preference Learning
Shuo Liu, Xinzichen Li, Tianle Chen +1
Several works have explored multi-agent reinforcement learning (MARL) in LLM collaboration. However, constructing reliable rewards is difficult in practice, as complete and accurat…
Cross-Modal Navigation with Multi-Agent Reinforcement Learning
Shuo Liu, Xinzichen Li, Christopher Amato
Robust embodied navigation relies on complementary sensory cues. However, high-quality and well-aligned multi-modal data is often difficult to obtain in practice. Training a monoli…
Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic
Shuo Liu, Tianle Chen, Ryan Amiri +1
Recent work has explored optimizing LLM collaboration through Multi-Agent Reinforcement Learning (MARL). However, most MARL fine-tuning approaches rely on predefined execution prot…
LLM Collaboration With Multi-Agent Reinforcement Learning
Shuo Liu, Tianle Chen, Zeyu Liang +2
A large amount of work has been done in Multi-Agent Systems (MAS) for modeling and solving problems with multiple interacting agents. However, most LLMs are pretrained independentl…
An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning
Christopher Amato
Multi-agent reinforcement learning (MARL) has exploded in popularity in recent years. Many approaches have been developed but they can be divided into three main types: centralized…
An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning
Christopher Amato
Multi-agent reinforcement learning (MARL) has exploded in popularity in recent years. While numerous approaches have been developed, they can be broadly categorized into three main…