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 1476–1500 of 1718 papers

TitleStatusHype
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—0
Decentralized Multi-Agent Reinforcement Learning with Global State Prediction—0
Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System—0
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network—0
Decentralized Policy Optimization—0
Decentralized Q-Learning in Zero-sum Markov Games—0
Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements—0
Decentralized scheduling through an adaptive, trading-based multi-agent system—0
Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network—0
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—0
Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective—0
Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning—0
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability—0
DeepHive: A multi-agent reinforcement learning approach for automated discovery of swarm-based optimization policies—0
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks—0
Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing—0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy—0
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—0
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
Deep Reinforcement Learning, a textbook—0
Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense—0
Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges—0
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified