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 1401–1425 of 1718 papers

TitleStatusHype
Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective—0
“Other-Play” for Zero-Shot Coordination—0
PAC Guarantees for Cooperative Multi-Agent Reinforcement Learning with Restricted Communication—0
Packet Routing with Graph Attention Multi-agent Reinforcement Learning—0
PAC Reinforcement Learning Algorithm for General-Sum Markov Games—0
Parallel Knowledge Transfer in Multi-Agent Reinforcement Learning—0
Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning—0
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning—0
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing—0
PathSeeker: Exploring LLM Security Vulnerabilities with a Reinforcement Learning-Based Jailbreak Approach—0
Paths to Equilibrium in Games—0
PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL—0
Sable: a Performant, Efficient and Scalable Sequence Model for MARL—0
Perimeter Control with Heterogeneous Metering Rates for Cordon Signals: A Physics-Regularized Multi-Agent Reinforcement Learning Approach—0
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach—0
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
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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