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 1651–1700 of 1718 papers

TitleStatusHype
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations—0
Deep Multi-Agent Reinforcement Learning with Relevance GraphsCode0
Emergence of linguistic conventions in multi-agent reinforcement learning—0
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
Bayesian Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Multi-Agent Common Knowledge Reinforcement LearningCode0
TarMAC: Targeted Multi-Agent Communication—0
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks—0
Multi-Agent Actor-Critic with Generative Cooperative Policy Network—0
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement LearningCode1
Actor-Attention-Critic for Multi-Agent Reinforcement LearningCode1
M^3RL: Mind-aware Multi-agent Management Reinforcement LearningCode0
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs—0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning—0
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning—0
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning—0
Negative Update Intervals in Deep Multi-Agent Reinforcement LearningCode1
Coordination-driven learning in multi-agent problem spaces—0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising—0
MARL-FWC: Optimal Coordination of Freeway Traffic Control Measures—0
Learning to Share and Hide Intentions using Information RegularizationCode0
Multi-Agent Reinforcement Learning: A Report on Challenges and ApproachesCode0
Learning to Act in Decentralized Partially Observable MDPs—0
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