Papers with Code 아카이브
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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/RL-WFU/multi_agent_attack
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/RaghavvGoel/marl_transfer_new
Learning Transferable Cooperative Behavior in Multi-Agent Teams
https://github.com/SihongHo/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/SintolRTOS/multi-agent_Example
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/Steven-Ho/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/Stippler/cow-simulator
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/TonghanWang/DOP
Value-Decomposition Networks For Cooperative Multi-Agent Learning
https://github.com/TonghanWang/NDQ
https://github.com/Wadaboa/cpr-appropriation
A multi-agent reinforcement learning model of common-pool resource appropriation
https://github.com/XiangboGaoBarry/TexasHoldemAI
https://github.com/Yuta123456/poker-ai
https://github.com/Yutongamber/MADDPG
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/ZifanWu/Coordinated-PPO
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/Zorrorulz/MultiAgentDDPG-Tennis
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/activatedgeek/marl-challenges-approaches
Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches
https://github.com/aerialroboticsgroup/agile-tethered-perching
https://github.com/alexis-jacq/LOLA
https://github.com/alexis-jacq/LOLA_DICE
https://github.com/alividl/abm-for-bond-markets
https://github.com/anonymous1234517/code
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
https://github.com/baradist/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/benedekrozemberczki/benedekrozemberczki
https://github.com/berndbischl/mlrMBO
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
https://github.com/bgLeon/EMG
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement Learning
https://github.com/biemann/Collaboration-and-Competition
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/biorobotics/PRD_environments
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/bonniesjli/MADDPG_Tennis
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/bonniesjli/MADDPG_Tennis_UnityML
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/caslab-vt/SARNet
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/caymansimpson/reuniclusVGC
https://github.com/chauncygu/safe-multi-agent-isaac-gym
https://github.com/chauncygu/safe-multi-agent-mujoco
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
https://github.com/chauncygu/safe-multi-agent-robosuite
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
https://github.com/cityflow-project/cityflower
CoLight: Learning Network-level Cooperation for Traffic Signal Control
https://github.com/ckjzsa/wwtp_opt_rl
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