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.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Subtasks


Latest papers with no code

MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification

no code yet • 26 Mar 2024

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

no code yet • 26 Mar 2024

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

no code yet • 26 Mar 2024

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

no code yet • 23 Mar 2024

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

no code yet • 23 Mar 2024

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

no code yet • 21 Mar 2024

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

no code yet • 21 Mar 2024

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

no code yet • 16 Mar 2024

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

no code yet • 13 Mar 2024

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

no code yet • 13 Mar 2024

We test our algorithm in an ITS environment with edge cloud computing.