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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 701725 of 1718 papers

TitleStatusHype
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement LearningCode1
Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation0
Information Design in Multi-Agent Reinforcement LearningCode1
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement LearningCode0
Multi-agent Continual Coordination via Progressive Task Contextualization0
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula0
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System0
System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent LearningCode1
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring0
Centralized control for multi-agent RL in a complex Real-Time-Strategy gameCode0
SEA: A Spatially Explicit Architecture for Multi-Agent Reinforcement Learning0
Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-AttentionCode0
Stubborn: An Environment for Evaluating Stubbornness between Agents with Aligned IncentivesCode0
SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control0
Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning0
Interpretability for Conditional Coordinated Behavior in Multi-Agent Reinforcement Learning0
Mastering Asymmetrical Multiplayer Game with Multi-Agent Asymmetric-Evolution Reinforcement Learning0
Heterogeneous-Agent Reinforcement LearningCode2
Graph Exploration for Effective Multi-agent Q-Learning0
Cooperative Multi-Agent Reinforcement Learning for Inventory Management0
Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach0
STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement LearningCode1
Language Instructed Reinforcement Learning for Human-AI CoordinationCode1
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning0
Multi-agent Policy Reciprocity with Theoretical Guarantee0
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Benchmark Results

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