Meta Learning for Multi-agent Communication
Recent works have shown remarkable progress in training artificial agents to understand natural language but are focused on using large amounts of raw data involving huge compute requirements. An interesting hypothesis follows the idea of training artificial agents via multi-agent communication while using small amounts of task-specific human data to ground the emergent language into natural language. This allows agents to communicate with humans without needing enormous expensive human demonstrations. Evolutionary studies have showed that simpler and easily adaptable languages arise as a result of communicating with a diverse group of large population. We propose to model this supposition with artificial agents and propose an adaptive population-based meta-reinforcement learning approach that builds such a population in an iterative manner. We show empirical results on referential games involving natural language where our agents outperform all baselines on both the task performance and language score including human evaluation. We demonstrate that our method induces constructive diversity into a growing population of agents that is beneficial in training the meta-agent.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversityMeta-LearningMeta Reinforcement LearningSimilar Papers 제목 키워드 기반
CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Games
Metaphors are a crucial way for humans to express complex or subtle ideas by comparing one concept to another, often from a different domain. However, many large language models (LLMs) struggle to interpret and apply met…
Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication
Online meta-learning is emerging as an enabling technique for achieving edge intelligence in the IoT ecosystem. Nevertheless, to learn a good meta-model for within-task fast adaptation, a single agent alone has to learn …
Meta-LearningMeta-CPR: Generalize to Unseen Large Number of Agents with Communication Pattern Recognition Module
Designing an effective communication mechanism among agents in reinforcement learning has been a challenging task, especially for real-world applications. The number of agents can grow or an environment sometimes needs t…
Meta Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)MetaMind: General and Cognitive World Models in Multi-Agent Systems by Meta-Theory of Mind
A major challenge for world models in multi-agent systems is to understand interdependent agent dynamics, predict interactive multi-agent trajectories, and plan over long horizons with collective awareness, without centr…
Enabling the Wireless Metaverse via Semantic Multiverse Communication
Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems, posing unprecedented challenges in terms of its multi-modal data transmissions with stringent latency and reliabilit…
Multi-agent Reinforcement LearningSemantic Communication