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 1626–1650 of 1718 papers

TitleStatusHype
Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving—0
Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach—0
Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning—0
Fairness in Multi-agent Reinforcement Learning for Stock Trading—0
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games—0
Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization—0
Fast Sequence Generation with Multi-Agent Reinforcement Learning—0
Federated Dynamic Spectrum Access—0
Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control—0
Federated Learning for Distributed Energy-Efficient Resource Allocation—0
LQR with Tracking: A Zeroth-order Approach and Its Global Convergence—0
Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding—0
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation—0
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control—0
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning—0
Few-Shot Teamwork—0
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games—0
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning—0
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning—0
Finite Horizon Multi-Agent Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems—0
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents—0
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation—0
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games—0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation—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