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Multi-agent Reinforcement Learning
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge
Artificial Generals Intelligence: Mastering Generals.io with Reinforcement Learning
SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning
The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind
Center of Gravity-Guided Focusing Influence Mechanism for Multi-Agent Reinforcement Learning
Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning
Transformer World Model for Sample Efficient Multi-Agent Reinforcement Learning
Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generation
Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study
Light Aircraft Game : Basic Implementation and training results analysis
Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning
MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering
Homeostatic Coupling for Prosocial Behavior
Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning
Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flow
Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation
When Is Diversity Rewarded in Cooperative Multi-Agent Learning?
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models
Curriculum Learning With Counterfactual Group Relative Policy Advantage For Multi-Agent Reinforcement Learning
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information
Ego-centric Learning of Communicative World Models for Autonomous Driving
Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments
Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games
A MARL-based Approach for Easing MAS Organization Engineering
Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods
CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients
LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation
Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning
Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation
RLAE: Reinforcement Learning-Assisted Ensemble for LLMs
Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning
Sorrel: A simple and flexible framework for multi-agent reinforcement learning
Information Structure in Mappings: An Approach to Learning, Representation, and Generalisation
Reward-Independent Messaging for Decentralized Multi-Agent Reinforcement Learning
Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective
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