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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 1601–1650 of 1718 papers

TitleStatusHype
Environmental-Impact Based Multi-Agent Reinforcement Learning—0
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
Escaping the State of Nature: A Hobbesian Approach to Cooperation in Multi-agent Reinforcement Learning—0
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning—0
Evaluating Generalization and Transfer Capacity of Multi-Agent Reinforcement Learning Across Variable Number of Agents—0
Evaluating Robustness of Cooperative MARL—0
Attacking c-MARL More Effectively: A Data Driven Approach—0
Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing—0
Evolutionary Dispersal of Ecological Species via Multi-Agent Deep Reinforcement Learning—0
Evolutionary Reinforcement Learning: A Survey—0
Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximation—0
Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning—0
Experience-replay Innovative Dynamics—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning—0
Exploiting hidden structures in non-convex games for convergence to Nash equilibrium—0
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning—0
Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank—0
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain—0
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning—0
Exploring Multi-Agent Reinforcement Learning for Unrelated Parallel Machine Scheduling—0
Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence—0
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
Facilitating Emergency Vehicle Passage in Congested Urban Areas Using Multi-agent Deep Reinforcement Learning—0
Fact-based Agent modeling for Multi-Agent Reinforcement Learning—0
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