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 951–1000 of 1718 papers

TitleStatusHype
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization—0
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning—0
Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems—0
Quantum Multi-Agent Meta Reinforcement Learning—0
Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation—0
Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks—0
Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning—0
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games—0
Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Delayed Observation—0
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning—0
Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination—0
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising—0
Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings—0
Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning—0
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning—0
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning—0
Selective Reincarnation: Offline-to-Online Multi-Agent Reinforcement Learning—0
Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning—0
Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution—0
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation—0
Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems—0
Regularization of the policy updates for stabilizing Mean Field Games—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center—0
Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems—0
Reinforcement Learning Based Robust Volt/Var Control in Active Distribution Networks With Imprecisely Known Delay—0
Multi-agent Reinforcement Learning for Decentralized Stable Matching—0
Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms—0
Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles—0
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions—0
Reinforcement Learning in Non-Stationary Discrete-Time Linear-Quadratic Mean-Field Games—0
Reinforcement Learning on Dyads to Enhance Medication Adherence—0
Reinforcement Learning With Reward Machines in Stochastic Games—0
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning—0
REMAX: Relational Representation for Multi-Agent Exploration—0
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning—0
Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning—0
Replication of Multi-agent Reinforcement Learning for the "Hide and Seek" Problem—0
Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning—0
Residual Q-Networks for Value Function Factorizing in Multi-Agent Reinforcement Learning—0
Resilient robot teams: a review integrating decentralised control, change-detection, and learning—0
Understanding Model Selection For Learning In Strategic Environments—0
REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes—0
Revealing Robust Oil and Gas Company Macro-Strategies using Deep Multi-Agent Reinforcement Learning—0
Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective—0
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning—0
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning—0
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning—0
Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing—0
Reward-Free Attacks in Multi-Agent Reinforcement Learning—0
Show:102550
← PrevPage 20 of 35Next →
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