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/hengyuan-hu/instruct-rl
Language Instructed Reinforcement Learning for Human-AI Coordination
https://github.com/hepengli/matrpo
https://github.com/hepengli/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/hjjimmykim/SchwabRoyale
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence
https://github.com/huaifeng1993/DFANet
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
https://github.com/iassael/learning-to-communicate
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
https://github.com/idsia/hhmarl_2d
Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering
https://github.com/ifpen/wfcrl-benchmark
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/impredicative/irc-rss-feed-bot
https://github.com/instadeepai/marl-eval
Towards a Standardised Performance Evaluation Protocol for Cooperative MARL
https://github.com/isp1tze/MAProj
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/j-a-lin/DFANet_PyTorch
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
https://github.com/jansenkeith501/CS295-MADDPG
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/jbr-ai-labs/BAROCCO
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments
https://github.com/jekyllstein/HORDOpt.jl
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
https://github.com/jiayu-ch15/MPE-for-curriculum-learning
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/jiayu-ch15/Variational-Automatic-Curriculum-Learning
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
https://github.com/jk96491/C-COMA
https://github.com/jk96491/SMAC
https://github.com/johannesharmse/multi_agent_RL
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/jyqhahah/rl_maddpg_matd3
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/kargarisaac/macrpo
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/karishnu/tf-agents-multi-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/karush17/emix
Energy-based Surprise Minimization for Multi-Agent Value Factorization
https://github.com/kcorder/qmix_variants
https://github.com/kfu02/jaxmarl
https://github.com/kinalmehta/epymarl
https://github.com/kishorkuttan/Multi-Agent-Deep-Deterministic-Policy-Gradient-actor-critic-deep-reinforcement-for-capacity-manageme
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/kornia/kornia
https://github.com/krasing/DRLearningCollaboration
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/ksajan/DDPG-MAPE
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/la3lma/Chez
Silly rules improve the capacity of agents to learn stable enforcement and compliance behaviors
https://github.com/la3lma/chezjulia
Silly rules improve the capacity of agents to learn stable enforcement and compliance behaviors
https://github.com/lachisis/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/leehe228/LogisticsEnv
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.