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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 581–590 of 1718 papers

TitleStatusHype
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning—0
Distributed Value Decomposition Networks with Networked Agents—0
CCL: Collaborative Curriculum Learning for Sparse-Reward Multi-Agent Reinforcement Learning via Co-evolutionary Task Evolution—0
Causal Multi-Agent Reinforcement Learning: Review and Open Problems—0
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep RL Approach—0
Causal Mean Field Multi-Agent Reinforcement Learning—0
Distributed Reinforcement Learning for Robot Teams: A Review—0
Causality Detection for Efficient Multi-Agent Reinforcement Learning—0
A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games—0
Show:102550
← PrevPage 59 of 172Next →
All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified