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 1026–1050 of 1718 papers

TitleStatusHype
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning—0
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning—0
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?—0
Safe and Efficient CAV Lane Changing using Decentralised Safety Shields—0
Safe Bottom-Up Flexibility Provision from Distributed Energy Resources—0
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving—0
Safe Multi-Agent Reinforcement Learning via Shielding—0
Safe Multi-agent Reinforcement Learning with Natural Language Constraints—0
Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium—0
Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Control—0
SA-MATD3:Self-attention-based multi-agent continuous control method in cooperative environments—0
Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD update—0
Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control—0
Sample-Efficient Multi-Agent RL: An Optimization Perspective—0
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning—0
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games—0
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty—0
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning—0
Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning—0
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems—0
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients—0
Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism—0
Scalable, Decentralized Multi-Agent Reinforcement Learning Methods Inspired by Stigmergy and Ant Colonies—0
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot—0
Scalable Hierarchical Reinforcement Learning for Hyper Scale Multi-Robot Task Planning—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