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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

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Showing 1326–1350 of 1718 papers

TitleStatusHype
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
Neural Recursive Belief States in Multi-Agent Reinforcement Learning—0
Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
Multi-Agent Reinforcement Learning with Temporal Logic SpecificationsCode1
Hybrid Information-driven Multi-agent Reinforcement Learning—0
Safe Multi-Agent Reinforcement Learning via Shielding—0
Data sharing gamesCode0
Fast Sequence Generation with Multi-Agent Reinforcement Learning—0
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with TransformersCode1
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned MessagingCode0
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning—0
Solving Common-Payoff Games with Approximate Policy IterationCode0
Coding for Distributed Multi-Agent Reinforcement Learning—0
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity—0
MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal ControlCode1
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels—0
Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers—0
Multi-Agent Trust Region LearningCode1
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients—0
Adaptive Learning Rates for Multi-Agent Reinforcement Learning—0
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium—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