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

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Showing 876–900 of 1718 papers

TitleStatusHype
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges—0
Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning—0
Explainable Multi-Agent Reinforcement Learning for Temporal QueriesCode0
Pragmatic Reasoning in Structured Signaling Games—0
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
Mixture of personality improved Spiking actor network for efficient multi-agent cooperation—0
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation—0
Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation—0
An Algorithm For Adversary Aware Decentralized Networked MARL—0
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement LearningCode0
Multi-agent Continual Coordination via Progressive Task Contextualization—0
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula—0
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System—0
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring—0
SEA: A Spatially Explicit Architecture for Multi-Agent Reinforcement Learning—0
Centralized control for multi-agent RL in a complex Real-Time-Strategy gameCode0
Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-AttentionCode0
Stubborn: An Environment for Evaluating Stubbornness between Agents with Aligned IncentivesCode0
Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning—0
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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