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 1326–1350 of 1718 papers

TitleStatusHype
Offline Decentralized Multi-Agent Reinforcement Learning—0
Flip Learning: Erase to Segment—0
Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training—0
Packet Routing with Graph Attention Multi-agent Reinforcement Learning—0
Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer—0
Communicating via Markov Decision ProcessesCode0
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty—0
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot—0
Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things—0
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication—0
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning—0
Traffic Signal Control with Communicative Deep Reinforcement Learning Agents: a Case Study—0
SA-MATD3:Self-attention-based multi-agent continuous control method in cooperative environments—0
Federated Dynamic Spectrum Access—0
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
Scientific multi-agent reinforcement learning for wall-models of turbulent flows—0
Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks—0
Many Agent Reinforcement Learning Under Partial Observability—0
Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning—0
A New Formalism, Method and Open Issues for Zero-Shot CoordinationCode0
Metric Policy Representations for Opponent Modeling—0
Learning to Play General-Sum Games Against Multiple Boundedly Rational AgentsCode0
Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective—0
Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search—0
Truthful Self-Play—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