SOTAVerified

Multi-agent Reinforcement Learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 676–700 of 1718 papers

TitleStatusHype
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning—0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility—0
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities—0
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?Code1
Attention Schema in Neural Agents—0
Reinforcement Learning With Reward Machines in Stochastic Games—0
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem—0
Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning—0
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning—0
Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning—0
Pragmatic Reasoning in Structured Signaling Games—0
Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges—0
Explainable Multi-Agent Reinforcement Learning for Temporal QueriesCode0
An Empirical Study on Google Research Football Multi-agent ScenariosCode1
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control—0
Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems—0
Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular NetworksCode0
Multi-Agent Reinforcement Learning for Network Routing in Integrated Access Backhaul Networks—0
Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning—0
Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges—0
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation—0
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackersCode1
Mixture of personality improved Spiking actor network for efficient multi-agent cooperation—0
An Algorithm For Adversary Aware Decentralized Networked MARL—0
Show:102550
← PrevPage 28 of 69Next →
All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified