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 1151–1200 of 1718 papers

TitleStatusHype
PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communications—0
Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with Graph Neural Network Communication Layer for Open-ended Wildfire-Management Resource Distribution—0
Resilient robot teams: a review integrating decentralised control, change-detection, and learning—0
Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximation—0
Federated Learning for Distributed Energy-Efficient Resource Allocation—0
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning—0
Learning to Transfer Role Assignment Across Team Sizes—0
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning—0
An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning—0
Multi-agent Actor-Critic with Time Dynamical Opponent Model—0
The Complexity of Markov Equilibrium in Stochastic Games—0
Distributed Reinforcement Learning for Robot Teams: A Review—0
RL4ReAl: Reinforcement Learning for Register Allocation—0
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction GamesCode0
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning—0
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach—0
Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning—0
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning—0
Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects—0
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty—0
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
A Survey of Multi-Agent Deep Reinforcement Learning with Communication—0
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning—0
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility—0
The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective—0
Impression Allocation and Policy Search in Display Advertising—0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework—0
On-the-fly Strategy Adaptation for ad-hoc Agent Coordination—0
Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility—0
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network—0
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning—0
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model—0
Can Mean Field Control (MFC) Approximate Cooperative Multi Agent Reinforcement Learning (MARL) with Non-Uniform Interaction?Code0
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions—0
A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning—0
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement LearningCode0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Cooperative Artificial IntelligenceCode0
PooL: Pheromone-inspired Communication Framework forLarge Scale Multi-Agent Reinforcement Learning—0
Shaping Advice in Deep Reinforcement LearningCode0
Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games—0
Motivating Physical Activity via Competitive Human-Robot Interaction—0
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning—0
Group-Agent Reinforcement Learning—0
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence—0
Attacking c-MARL More Effectively: A Data Driven Approach—0
Generalization in Cooperative Multi-Agent Systems—0
Trust Region Bounds for Decentralized PPO Under Non-stationarity—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