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 1551–1575 of 1718 papers

TitleStatusHype
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning—0
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning—0
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth—0
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks—0
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge—0
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning—0
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning—0
Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning—0
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network—0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning—0
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games—0
Efficiently Quantifying Individual Agent Importance in Cooperative MARL—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning—0
Efficient Replay Memory Architectures in Multi-Agent Reinforcement Learning for Traffic Congestion Control—0
Efficient Training in Multi-Agent Reinforcement Learning: A Communication-Free Framework for the Box-Pushing Problem—0
Efficient UAV Trajectory-Planning using Economic Reinforcement Learning—0
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance—0
Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football—0
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning—0
Emergence of linguistic conventions in multi-agent reinforcement learning—0
Emergence of Roles in Robotic Teams with Model Sharing and Limited Communication—0
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning—0
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Benchmark Results

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