Learning When To Communicate At Scale In Multiagent Cooperative And Competitive Tasks

Abstract

Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings. IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues. Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases. Our results convey that IC3Net agents learn when to communicate ba

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keysingh2018learning

Related papers

• Learning Practical Communication Strategies In Cooperative Multi-agent Reinforcement Learning (2022)0.00
• Accnet: Actor-coordinator-critic Net For "learning-to-communicate" With Deep Multi-agent Reinforcement Learning (2017)0.00
• Mixed Cooperative-competitive Communication Using Multi-agent Reinforcement Learning (2021)5.84
• AC2C: Adaptively Controlled Two-hop Communication For Multi-agent Reinforcement Learning (2023)0.00
• Learning To Schedule Communication In Multi-agent Reinforcement Learning (2019)0.00
• Learning Individually Inferred Communication For Multi-agent Cooperation (2020)0.00
• Minimizing Communication While Maximizing Performance In Multi-agent Reinforcement Learning (2021)0.00
• Low-bandwidth Communication Emerges Naturally In Multi-agent Learning Systems (2020)0.00