LDSA: Learning Dynamic Subtask Assignment In Cooperative Multi-agent Reinforcement Learning

Abstract

Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in recent years. For training efficiency and scalability, most of the MARL algorithms make all agents share the same policy or value network. However, in many complex multi-agent tasks, different agents are expected to possess specific abilities to handle different subtasks. In those scenarios, sharing parameters indiscriminately may lead to similar behavior across all agents, which will limit the exploration efficiency and degrade the final performance. To balance the training complexity and the diversity of agent behavior, we propose a novel framework to learn dynamic subtask assignment (LDSA) in cooperative MARL. Specifically, we first introduce a subtask encoder to construct a vector representation for each subtask according to its identity. To reasonably assign agents to different subtasks, we propose an ability-based subtask selection strategy, which can dynamically group agents with similar abiliti

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keyyang2022ldsa

Related papers

• Individual Specialization In Multi-task Environments With Multiagent Reinforcement Learners (2019)0.00
• Learning To Share In Multi-agent Reinforcement Learning (2021)0.00
• Policy Distillation And Value Matching In Multiagent Reinforcement Learning (2019)10.48
• ALMA: Hierarchical Learning For Composite Multi-agent Tasks (2022)0.00
• Multi-agent Continual Coordination Via Progressive Task Contextualization (2023)5.24
• Locality Matters: A Scalable Value Decomposition Approach For Cooperative Multi-agent Reinforcement Learning (2021)0.00
• Benchmarking Multi-agent Deep Reinforcement Learning Algorithms In Cooperative Tasks (2020)0.00
• MARL-LNS: Cooperative Multi-agent Reinforcement Learning Via Large Neighborhoods Search (2024)0.00