A review of cooperation in multi-agent learning
Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …
disciplines, including game theory, economics, social sciences, and evolutionary biology …
Proagent: Building proactive cooperative ai with large language models
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in
the realm of multi-agent systems. Current approaches to developing cooperative agents rely …
the realm of multi-agent systems. Current approaches to developing cooperative agents rely …
A survey of progress on cooperative multi-agent reinforcement learning in open environment
Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
An efficient end-to-end training approach for zero-shot human-AI coordination
The goal of zero-shot human-AI coordination is to develop an agent that can collaborate with
humans without relying on human data. Prevailing two-stage population-based methods …
humans without relying on human data. Prevailing two-stage population-based methods …
Learning zero-shot cooperation with humans, assuming humans are biased
There is a recent trend of applying multi-agent reinforcement learning (MARL) to train an
agent that can cooperate with humans in a zero-shot fashion without using any human data …
agent that can cooperate with humans in a zero-shot fashion without using any human data …
Cooperative open-ended learning framework for zero-shot coordination
Zero-shot coordination in cooperative artificial intelligence (AI) remains a significant
challenge, which means effectively coordinating with a wide range of unseen partners …
challenge, which means effectively coordinating with a wide range of unseen partners …
Evaluating multi-agent coordination abilities in large language models
A pivotal aim in contemporary AI research is to develop agents proficient in multi-agent
coordination, enabling effective collaboration with both humans and other systems. Large …
coordination, enabling effective collaboration with both humans and other systems. Large …
Iteratively learn diverse strategies with state distance information
In complex reinforcement learning (RL) problems, policies with similar rewards may have
substantially different behaviors. It remains a fundamental challenge to optimize rewards …
substantially different behaviors. It remains a fundamental challenge to optimize rewards …
Llm-powered hierarchical language agent for real-time human-ai coordination
AI agents powered by Large Language Models (LLMs) have made significant advances,
enabling them to assist humans in diverse complex tasks and leading to a revolution in …
enabling them to assist humans in diverse complex tasks and leading to a revolution in …
Pecan: Leveraging policy ensemble for context-aware zero-shot human-ai coordination
Zero-shot human-AI coordination holds the promise of collaborating with humans without
human data. Prevailing methods try to train the ego agent with a population of partners via …
human data. Prevailing methods try to train the ego agent with a population of partners via …