Multi-agent Reinforcement Learning
388 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
Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves
Our results show that the transformer model of moderate depth with gated residual connections around the multi-head attention, multi-layer perceptron, and the transformer block (STrXL) proposed in this paper is optimal and boosts energy efficiency by an average of 22. 1% for these complex spread waves over the existing spring damper (SD) controller.
Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal Hypergraphs
Traffic signal control systems (TSCSs) are integral to intelligent traffic management, fostering efficient vehicle flow.
N-Agent Ad Hoc Teamwork
POAM is a policy gradient, multi-agent reinforcement learning approach to the NAHT problem, that enables adaptation to diverse teammate behaviors by learning representations of teammate behaviors.
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
This is the first theoretical result for randomized exploration in cooperative MARL.
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency.
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.
