Multi-agent Reinforcement Learning
380 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.
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Latest papers with no code
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.
Paths to Equilibrium in Normal-Form Games
In multi-agent reinforcement learning (MARL), agents repeatedly interact across time and revise their strategies as new data arrives, producing a sequence of strategy profiles.
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph
This paper proposes a novel hierarchical MARL model called Hierarchical Cooperation Graph Learning (HCGL) for solving general multi-agent problems.
Multi-agent transformer-accelerated RL for satisfaction of STL specifications
One of the main challenges in multi-agent reinforcement learning is scalability as the number of agents increases.
Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD update
This leads to an interesting open question: Can the local TD-update approach entail low sample and communication complexities?
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide.
Emergent communication and learning pressures in language models: a language evolution perspective
Based on a short literature review, we identify key pressures that have recovered initially absent human patterns in emergent communication models: communicative success, efficiency, learnability, and other psycho-/sociolinguistic factors.
A Scalable and Parallelizable Digital Twin Framework for Sustainable Sim2Real Transition of Multi-Agent Reinforcement Learning Systems
We introduce AutoDRIVE Ecosystem as an enabling digital twin framework to train, deploy, and transfer cooperative as well as competitive multi-agent reinforcement learning policies from simulation to reality.
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning (MARL) algorithms face the challenge of efficient exploration due to the exponential increase in the size of the joint state-action space.
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning
We test our algorithm in an ITS environment with edge cloud computing.