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

Showing 961970 of 1718 papers

TitleStatusHype
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 System0
Functional Optimization Reinforcement Learning for Real-Time Bidding0
Toward multi-target self-organizing pursuit in a partially observable Markov gameCode1
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement LearningCode0
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems0
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?0
From Multi-agent to Multi-robot: A Scalable Training and Evaluation Platform for Multi-robot Reinforcement Learning0
Logic-based Reward Shaping for Multi-Agent Reinforcement LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified