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 1171–1180 of 1718 papers

TitleStatusHype
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
A Survey of Multi-Agent Deep Reinforcement Learning with Communication—0
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning—0
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility—0
The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective—0
Impression Allocation and Policy Search in Display Advertising—0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework—0
On-the-fly Strategy Adaptation for ad-hoc Agent Coordination—0
Show:102550
← PrevPage 118 of 172Next →
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