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 801–850 of 1718 papers

TitleStatusHype
CURO: Curriculum Learning for Relative Overgeneralization—0
Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate—0
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning—0
Human-Machine Dialogue as a Stochastic Game—0
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System—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
Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Learning Cooperative Multi-Agent Policies with Partial Reward Decoupling—0
Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning—0
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning—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
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach—0
A MARL-based Approach for Easing MAS Organization Engineering—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
A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation—0
Learning Existing Social Conventions via Observationally Augmented Self-Play—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 from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games—0
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning—0
LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning—0
Learning Homophilic Incentives in Sequential Social Dilemmas—0
Homeostatic Coupling for Prosocial Behavior—0
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems—0
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning—0
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
Learning Mean Field Control on Sparse Graphs—0
Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach—0
Learning Meta Representations for Agents in 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
Learning Multi-agent Multi-machine Tending by Mobile Robots—0
Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints—0
Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm—0
Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation—0
Automating Turbulence Modeling by Multi-Agent Reinforcement Learning—0
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning—0
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems—0
Learning Roles with Emergent Social Value Orientations—0
Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environments—0
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense—0
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks—0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
Cross-layer Band Selection and Routing Design for Diverse Band-aware DSA Networks—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