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 1451–1475 of 1718 papers

TitleStatusHype
Credit-cognisant reinforcement learning for multi-agent cooperation—0
Cross-layer Band Selection and Routing Design for Diverse Band-aware DSA Networks—0
Crowd-sensing Enhanced Parking Patrol using Trajectories of Sharing Bikes—0
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems—0
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning—0
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning—0
CURO: Curriculum Learning for Relative Overgeneralization—0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning—0
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning—0
DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training—0
Group-Agent Reinforcement Learning—0
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning—0
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition—0
Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication—0
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control—0
Decentralized Deterministic Multi-Agent Reinforcement Learning—0
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks—0
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning—0
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances—0
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty—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