Partially Observable Mean Field Multi-Agent Reinforcement Learning with Graph-Attention
The author proposes a novel approach, GAMFQ, using Graph-Attention to select important neighborhood agents for effective multi-agent reinforcement learning.
Decentralized Multi-Agent Reinforcement Learning for Space Robot Motion Planning
The author proposes a decentralized multi-agent reinforcement learning paradigm for trajectory planning and base reorientation tasks for multi-arm space robots. By hierarchically assigning control tasks to different agents, the approach improves exploration efficiency and robustness.
Independent Reinforcement Learning for Cooperative-Competitive Agents: A Mean-Field Perspective
Developing a novel algorithm, MRPG, for achieving Nash equilibrium in General-Sum LQ Mean-Field Type Games.
Multi-Agent Transformer-Accelerated RL for Satisfaction of STL Specifications
Efficiently solving temporally dependent multi-agent problems using transformers.
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification
Introducing MA4DIV, a novel approach using Multi-Agent reinforcement learning for search result diversification, addressing limitations of existing methods.
Bottom-Up Network (BUN): A Novel Approach for Sparse and Decentralized Multi-Agent Reinforcement Learning
The Bottom-Up Network (BUN) approach tackles scalability challenges in multi-agent reinforcement learning by initializing a sparse network that promotes independent agent learning and dynamically establishes connections based on gradient information, enabling efficient coordination while minimizing communication costs.
Exploration-Enhanced Equivariant Graph Neural Networks for Improved Sample Efficiency and Generalization in Multi-Agent Reinforcement Learning
This paper introduces E2GN2, a novel approach leveraging equivariant graph neural networks to significantly enhance sample efficiency and generalization in multi-agent reinforcement learning tasks.
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