Minimizing Communication While Maximizing Performance In Multi-agent Reinforcement Learning

Abstract

Inter-agent communication can significantly increase performance in multi-agent tasks that require co-ordination to achieve a shared goal. Prior work has shown that it is possible to learn inter-agent communication protocols using multi-agent reinforcement learning and message-passing network architectures. However, these models use an unconstrained broadcast communication model, in which an agent communicates with all other agents at every step, even when the task does not require it. In real-world applications, where communication may be limited by system constraints like bandwidth, power and network capacity, one might need to reduce the number of messages that are sent. In this work, we explore a simple method of minimizing communication while maximizing performance in multi-task learning: simultaneously optimizing a task-specific objective and a communication penalty. We show that the objectives can be optimized using Reinforce and the Gumbel-Softmax reparameterization. We introdu

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyvijay2021minimizing

Related papers

• Learning Practical Communication Strategies In Cooperative Multi-agent Reinforcement Learning (2022)0.00
• Multi-agent Reinforcement Learning With Communication-constrained Priors (2025)0.00
• Rgmcomm: Return Gap Minimization Via Discrete Communications In Multi-agent Reinforcement Learning (2023)6.77
• Learning To Schedule Communication In Multi-agent Reinforcement Learning (2019)0.00
• Effective Communications: A Joint Learning And Communication Framework For Multi-agent Reinforcement Learning Over Noisy Channels (2021)0.00
• Improving Coordination In Small-scale Multi-agent Deep Reinforcement Learning Through Memory-driven Communication (2019)12.25
• Event-triggered Multi-agent Reinforcement Learning With Communication Under Limited-bandwidth Constraint (2020)0.00
• Exploring Task-oriented Communication In Multi-agent System: A Deep Reinforcement Learning Approach (2022)0.00