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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 576–600 of 1718 papers

TitleStatusHype
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel—0
Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning—0
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels—0
Coordinated Power Smoothing Control for Wind Storage Integrated System with Physics-informed Deep Reinforcement Learning—0
Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications—0
A Tensor Network Implementation of Multi Agent Reinforcement Learning—0
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach—0
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning—0
AIR: Unifying Individual and Collective Exploration in Cooperative Multi-Agent Reinforcement Learning—0
Adaptive Learning Rates for Multi-Agent Reinforcement Learning—0
Cooperative Reward Shaping for Multi-Agent Pathfinding—0
Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications—0
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach—0
Cooperative Path Planning with Asynchronous Multiagent Reinforcement Learning—0
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment—0
Unsynchronized Decentralized Q-Learning: Two Timescale Analysis By Persistence—0
Cooperative Multi-Agent Reinforcement Learning for Inventory Management—0
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning—0
AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network—0
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning—0
From Motor Control to Team Play in Simulated Humanoid Football—0
Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey—0
Generalisable Agents for Neural Network Optimisation—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