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 701–725 of 1718 papers

TitleStatusHype
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games—0
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning—0
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning—0
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play—0
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System—0
Human-Machine Dialogue as a Stochastic Game—0
Hybrid Information-driven Multi-agent Reinforcement Learning—0
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning—0
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning—0
AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning—0
Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning—0
Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning—0
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks—0
IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning for Missing Training Data—0
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games—0
ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control—0
Few-Shot Teamwork—0
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning—0
Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning—0
Implementations that Matter in Cooperative Multi-Agent Reinforcement Learning—0
Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning—0
Impression Allocation and Policy Search in Display Advertising—0
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space—0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—0
Show:102550
← PrevPage 29 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