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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 151175 of 1718 papers

TitleStatusHype
Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraftCode1
Interaction Pattern Disentangling for Multi-Agent Reinforcement LearningCode1
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward FunctionsCode1
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement LearningCode1
Toward multi-target self-organizing pursuit in a partially observable Markov gameCode1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with TransformerCode1
ALMA: Hierarchical Learning for Composite Multi-Agent TasksCode1
QGNN: Value Function Factorisation with Graph Neural NetworksCode1
Scalable Multi-Agent Model-Based Reinforcement LearningCode1
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam SearchCode1
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication MethodCode1
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit DesignCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information CollaborationCode1
Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer EmpowermentCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
The Shapley Value in Machine LearningCode1
Multi-Agent Path Finding with Prioritized Communication LearningCode1
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement LearningCode1
Learning to Share in Multi-Agent Reinforcement LearningCode1
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC TasksCode1
Neural Auto-Curricula in Two-Player Zero-Sum GamesCode1
VAST: Value Function Factorization with Variable Agent Sub-TeamsCode1
Regularized Softmax Deep Multi-Agent Q-LearningCode1
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Benchmark Results

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