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 1101–1125 of 1718 papers

TitleStatusHype
Few-Shot Teamwork—0
Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning as a Framework for Emergent Behavior—0
Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework—0
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning—0
Decentralized scheduling through an adaptive, trading-based multi-agent system—0
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning SystemsCode0
EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System—0
Functional Optimization Reinforcement Learning for Real-Time Bidding—0
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement LearningCode0
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems—0
From Multi-agent to Multi-robot: A Scalable Training and Evaluation Platform for Multi-robot Reinforcement Learning—0
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?—0
Logic-based Reward Shaping for Multi-Agent Reinforcement LearningCode0
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning—0
Universally Expressive Communication in Multi-Agent Reinforcement LearningCode0
Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs—0
Finite-Time Analysis of Fully Decentralized Single-Timescale Actor-Critic—0
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy—0
Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling—0
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning—0
Policy Optimization for Markov Games: Unified Framework and Faster Convergence—0
MACC: Cross-Layer Multi-Agent Congestion Control with Deep Reinforcement Learning—0
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning—0
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential GameCode0
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games—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