SOTAVerified

SMAC

Bechmarks for Efficient Exploration of Completion of Multi-stage Tasks and Usage of Environmental Factors

Papers

Showing 1–10 of 121 papers

TitleStatusHype
Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic GenerationCode0
Curriculum Learning With Counterfactual Group Relative Policy Advantage For Multi-Agent Reinforcement LearningCode1
Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods—0
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning—0
POCAII: Parameter Optimization with Conscious Allocation using Iterative Intelligence—0
JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 MinutesCode1
Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Delayed Observation—0
Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent CooperationCode0
AVA: Attentive VLM Agent for Mastering StarCraft IICode1
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DDNMedian Win Rate91.48—Unverified
2DPLEXMedian Win Rate90.62—Unverified
3DMIXMedian Win Rate85.45—Unverified
4QMIXMedian Win Rate84.77—Unverified
5QPLEXMedian Win Rate78.12—Unverified
6VDNMedian Win Rate63.12—Unverified
7QMIXMedian Win Rate49—Unverified
8QMIXMedian Win Rate49—Unverified
9DIQLMedian Win Rate6.02—Unverified
10IQLMedian Win Rate2.27—Unverified