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

Showing 401425 of 1718 papers

TitleStatusHype
Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement LearningCode0
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy GradientCode0
Cooperative Patrol Routing: Optimizing Urban Crime Surveillance through Multi-Agent Reinforcement LearningCode0
Multi-lingual agents through multi-headed neural networksCode0
Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning SystemsCode0
IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social DilemmasCode0
Investigating Relational State Abstraction in Collaborative MARLCode0
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement LearningCode0
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized TeamingCode0
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential GameCode0
Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium controlCode0
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement LearningCode0
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement LearningCode0
Independent Learning in Constrained Markov Potential GamesCode0
Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement LearningCode0
Communicating via Markov Decision ProcessesCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Cooperative Artificial IntelligenceCode0
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement LearningCode0
Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile RobotsCode0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
Cooperation Dynamics in Multi-Agent Systems: Exploring Game-Theoretic Scenarios with Mean-Field EquilibriaCode0
Adaptive and Robust DBSCAN with Multi-agent Reinforcement LearningCode0
Reinforcement Learning from Hierarchical CriticsCode0
Health-Informed Policy Gradients for Multi-Agent Reinforcement LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified