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/code-implementation1/Code1/tree/main/DecoMR
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning
https://github.com/cogitoergoread/rlcard3
https://github.com/columbia-robovision/decentralized-multiarm
https://github.com/coperception/coperception
Who2com: Collaborative Perception via Learnable Handshake Communication
https://github.com/cran/mlrMBO
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
https://github.com/crowdAI/marLo
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
https://github.com/cts198859/deeprl_dist
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
https://github.com/cyanrain7/trpo-in-marl
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/cyoon1729/Multi-agent-reinforcement-learning
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/cyrusneary/rm-cooperative-marl
Reward Machines for Cooperative Multi-Agent Reinforcement Learning
https://github.com/czh513/Hierarchical-Critics-Assignment-for-Multi-agent-Reinforcement-Learning
https://github.com/czh513/RL-Hierarchical-Critics
https://github.com/czh513/Reinforcement-Learning-from-Hierarchical-Critics
https://github.com/darshil333/CSE574
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/datamllab/rlcard-tutorial
https://github.com/debajit15kgp/multiagent-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/dshchung/local_minmax_gans
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
https://github.com/dtabas/epymarl
https://github.com/dtabas/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/eduardosebastianrodriguez/phmarl
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
https://github.com/emerge-lab/nocturne_lab
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/emiliojorge/Inventing-a-Grounded-Language
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
https://github.com/esmeralday/MARL
https://github.com/fabien-couthouis/xai-in-rl
A multi-agent reinforcement learning model of common-pool resource appropriation
https://github.com/facebookresearch/Hanabi_SPARTA
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
https://github.com/facebookresearch/hanabi
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
https://github.com/facebookresearch/jps
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
https://github.com/fdcl-gwu/gym-rotor
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
https://github.com/geminiln/ai4gcc
https://github.com/gingkg/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/gingkg/smac
https://github.com/goldbattle/snakes_mal
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/goodbyeearth/myVBC
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
https://github.com/google/maddpg-replication
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/hanhanAnderson/LSF-SAC
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.