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 12511275 of 1718 papers

TitleStatusHype
Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search0
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Truthful Self-Play0
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement LearningCode1
Decentralized Q-Learning in Zero-sum Markov Games0
Transferable and Distributed User Association Policies for 5G and Beyond Networks0
Neural Auto-CurriculaCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment0
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning0
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning0
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-LearningCode1
MARL with General Utilities via Decentralized Shadow Reward Actor-Critic0
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning0
From Motor Control to Team Play in Simulated Humanoid Football0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound0
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach0
Dependent Multi-Task Learning with Causal Intervention for Image Captioning0
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning0
Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environments0
AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning0
Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees0
Scalable, Decentralized Multi-Agent Reinforcement Learning Methods Inspired by Stigmergy and Ant Colonies0
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Benchmark Results

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