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 901–950 of 1718 papers

TitleStatusHype
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning—0
PIMAEX: Multi-Agent Exploration through Peer Incentivization—0
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL—0
Policy Diversity for Cooperative Agents—0
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response—0
Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games—0
Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games—0
Policy Optimization for Markov Games: Unified Framework and Faster Convergence—0
Polymatrix Competitive Gradient Descent—0
PooL: Pheromone-inspired Communication Framework forLarge Scale Multi-Agent Reinforcement Learning—0
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information—0
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control—0
POWQMIX: Weighted Value Factorization with Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning—0
PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communications—0
Pragmatic Reasoning in Structured Signaling Games—0
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities—0
Predicting Multi-Agent Specialization via Task Parallelizability—0
Pretrained LLMs as Real-Time Controllers for Robot Operated Serial Production Line—0
Prioritized Guidance for Efficient Multi-Agent Reinforcement Learning Exploration—0
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning—0
Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning—0
Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains—0
Proactive Multi-Camera Collaboration For 3D Human Pose Estimation—0
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning—0
Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach—0
Probe-Based Interventions for Modifying Agent Behavior—0
Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming—0
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help—0
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning—0
Collaboration Promotes Group Resilience in Multi-Agent AI—0
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning—0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation—0
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning—0
Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning—0
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication—0
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus—0
Online Learning in Unknown Markov Games—0
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
Provably Learning Nash Policies in Constrained Markov Potential Games—0
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning—0
Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning—0
QFree: A Universal Value Function Factorization for Multi-Agent Reinforcement Learning—0
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis—0
QLLM: Do We Really Need a Mixing Network for Credit Assignment in Multi-Agent Reinforcement Learning?—0
Q-MARL: A GRAPH-BASED SOLUTION FOR LARGE-SCALE MULTI-AGENT REINFORCEMENT LEARNING INSPIRED BY QUANTUM CHEMISTRY—0
Q-MARL: A quantum-inspired algorithm using neural message passing for large-scale multi-agent reinforcement learning—0
QMNet: Importance-Aware Message Exchange for Decentralized Multi-Agent Reinforcement Learning—0
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning—0
QR-MIX: Distributional Value Function Factorisation for Cooperative Multi-Agent Reinforcement Learning—0
Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things—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