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 1071–1080 of 1718 papers

TitleStatusHype
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning—0
Shapley Value Based Multi-Agent Reinforcement Learning: Theory, Method and Its Application to Energy Network—0
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles—0
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning—0
Teaching on a Budget in Multi-Agent Deep Reinforcement Learning—0
Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents—0
Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies—0
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial—0
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks—0
Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning—0
Show:102550
← PrevPage 108 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