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 801–825 of 1718 papers

TitleStatusHype
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning—0
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach—0
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning—0
A MARL-based Approach for Easing MAS Organization Engineering—0
A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation—0
Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning—0
Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning—0
Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors—0
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning—0
Learning Cooperative Multi-Agent Policies with Partial Reward Decoupling—0
Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning—0
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games—0
Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning—0
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning—0
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning—0
Homeostatic Coupling for Prosocial Behavior—0
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach—0
Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments—0
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems—0
Learning Existing Social Conventions via Observationally Augmented Self-Play—0
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning—0
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning—0
Crowd-sensing Enhanced Parking Patrol using Trajectories of Sharing Bikes—0
Show:102550
← PrevPage 33 of 69Next →
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