Multi-agent Reinforcement Learning
389 papers with code • 3 benchmarks • 9 datasets
The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.
Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
Benchmarks
These leaderboards are used to track progress in Multi-agent Reinforcement Learning
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Libraries
Use these libraries to find Multi-agent Reinforcement Learning models and implementationsDatasets
CityFlow
StarCraft II Learning Environment
SMAC-Exp
Hanabi Learning Environment
OG-MARL
pursuitMW
ColosseumRL
Latest papers with no code
Differentially Private Reinforcement Learning with Self-Play
We study the problem of multi-agent reinforcement learning (multi-agent RL) with differential privacy (DP) constraints.
Attention-Driven Multi-Agent Reinforcement Learning: Enhancing Decisions with Expertise-Informed Tasks
In this paper, we introduce an alternative approach to enhancing Multi-Agent Reinforcement Learning (MARL) through the integration of domain knowledge and attention-based policy mechanisms.
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration
Second, we introduce a heterogeneous layer for decision-making, whose parameters are specifically generated by the learned latent variables.
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks
Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies, considering both oblivious (where decision-making is independent of the physical processes) and non-oblivious policies (where decision-making depends on physical processes).
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search
Cooperative multi-agent reinforcement learning (MARL) has been an increasingly important research topic in the last half-decade because of its great potential for real-world applications.
Distributed Autonomous Swarm Formation for Dynamic Network Bridging
Effective operation and seamless cooperation of robotic systems are a fundamental component of next-generation technologies and applications.
Multi-Agent Reinforcement Learning with Control-Theoretic Safety Guarantees for Dynamic Network Bridging
Addressing complex cooperative tasks in safety-critical environments poses significant challenges for Multi-Agent Systems, especially under conditions of partial observability.
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning
We study the efficiency of the emergent policy and identify convergence in agent size and swim speeds.
EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management
This paper investigates the increasing roles of Renewable Energy Sources (RES) and Electric Vehicles (EVs).
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification
The objective of search result diversification (SRD) is to ensure that selected documents cover as many different subtopics as possible.
