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 1501–1550 of 1718 papers

TitleStatusHype
Multi-Agent Informational Learning Processes—0
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward—0
Multi-Agent Reinforcement Learning in Stochastic Networked SystemsCode0
Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation—0
Skill Discovery of Coordination in Multi-agent Reinforcement Learning—0
Incorporating Pragmatic Reasoning Communication into Emergent Language—0
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization—0
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement LearningCode0
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization—0
Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning—0
Automating Turbulence Modeling by Multi-Agent Reinforcement Learning—0
Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning—0
Gifting in multi-agent reinforcement learningCode0
Multi-agent Reinforcement Learning for Decentralized Stable Matching—0
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information—0
Learning Expensive Coordination: An Event-Based Deep RL Approach—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
Macro-Action-Based Deep Multi-Agent Reinforcement Learning—0
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning—0
Networked Multi-Agent Reinforcement Learning with Emergent Communication—0
Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing—0
A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control—0
Multi-agent Reinforcement Learning for Networked System Control—0
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement LearningCode0
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication—0
Parallel Knowledge Transfer in Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward—0
A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation AssuranceCode0
Value Variance Minimization for Learning Approximate Equilibrium in Aggregation Systems—0
A General Framework for Learning Mean-Field Games—0
A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles—0
Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing—0
Dynamic Queue-Jump Lane for Emergency Vehicles under Partially Connected Settings: A Multi-Agent Deep Reinforcement Learning Approach—0
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games—0
Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges—0
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning—0
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement LearningCode0
Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach—0
Learning Multi-Agent Coordination through Connectivity-driven Communication—0
Learning Structured Communication for Multi-agent Reinforcement Learning—0
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis—0
Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming—0
Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems—0
Silly rules improve the capacity of agents to learn stable enforcement and compliance behaviorsCode0
On Solving Cooperative MARL Problems with a Few Good Experiences—0
Algorithms in Multi-Agent Systems: A Holistic Perspective from Reinforcement Learning and Game Theory—0
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping—0
Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss—0
Multi-Robot Formation Control Using 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