Camel: Communicative agents for" mind" exploration of large language model society
The rapid advancement of chat-based language models has led to remarkable progress in
complex task-solving. However, their success heavily relies on human input to guide the …
complex task-solving. However, their success heavily relies on human input to guide the …
Multi-agent reinforcement learning: Methods, applications, visionary prospects, and challenges
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …
technique. However, current studies and applications need to address its scalability, non …
Efficient and scalable reinforcement learning for large-scale network control
The primary challenge in the development of large-scale artificial intelligence (AI) systems
lies in achieving scalable decision-making—extending the AI models while maintaining …
lies in achieving scalable decision-making—extending the AI models while maintaining …
Mindstorms in natural language-based societies of mind
Both Minsky's" society of mind" and Schmidhuber's" learning to think" inspire diverse
societies of large multimodal neural networks (NNs) that solve problems by interviewing …
societies of large multimodal neural networks (NNs) that solve problems by interviewing …
[PDF][PDF] A survey of multi-agent reinforcement learning with communication
Communication is an effective mechanism for coordinating the behavior of multiple agents.
In the field of multi-agent reinforcement learning, agents can improve the overall learning …
In the field of multi-agent reinforcement learning, agents can improve the overall learning …
Distributed reinforcement learning for robot teams: A review
Abstract Purpose of Review Recent advances in sensing, actuation, and computation have
opened the door to multi-robot systems consisting of hundreds/thousands of robots, with …
opened the door to multi-robot systems consisting of hundreds/thousands of robots, with …
Multi-agent incentive communication via decentralized teammate modeling
Effective communication can improve coordination in cooperative multi-agent reinforcement
learning (MARL). One popular communication scheme is exchanging agents' local …
learning (MARL). One popular communication scheme is exchanging agents' local …
Asynchronous actor-critic for multi-agent reinforcement learning
Synchronizing decisions across multiple agents in realistic settings is problematic since it
requires agents to wait for other agents to terminate and communicate about termination …
requires agents to wait for other agents to terminate and communicate about termination …
Efficient multi-agent communication via self-supervised information aggregation
Utilizing messages from teammates can improve coordination in cooperative Multi-agent
Reinforcement Learning (MARL). To obtain meaningful information for decision-making …
Reinforcement Learning (MARL). To obtain meaningful information for decision-making …
Rethinking individual global max in cooperative multi-agent reinforcement learning
In cooperative multi-agent reinforcement learning, centralized training and decentralized
execution (CTDE) has achieved remarkable success. Individual Global Max (IGM) …
execution (CTDE) has achieved remarkable success. Individual Global Max (IGM) …