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 901–925 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
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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