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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 1676–1700 of 1718 papers

TitleStatusHype
Learning Existing Social Conventions via Observationally Augmented Self-Play—0
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization—0
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients—0
On Gradient-Based Learning in Continuous Games—0
Emergent Communication through Negotiation—0
Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue ModellingCode0
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
Entropy based Independent Learning in Anonymous Multi-Agent Settings—0
Inequity aversion improves cooperation in intertemporal social dilemmasCode1
Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings—0
Intent-aware Multi-agent Reinforcement Learning—0
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising—0
Modeling Others using Oneself in Multi-Agent Reinforcement Learning—0
Fully Decentralized Multi-Agent Reinforcement Learning with Networked AgentsCode1
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning—0
Learning to Gather without CommunicationCode0
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet ManagementCode0
Mean Field Multi-Agent Reinforcement LearningCode1
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication—0
Neuron as an Agent—0
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective IntelligenceCode0
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning—0
Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning—0
Learning with Opponent-Learning AwarenessCode0
Prosocial learning agents solve generalized Stag Hunts better than selfish onesCode0
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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