https://github.com/j-a-lin/DFANet_PyTorch
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
Papers with Code 아카이브
SMAC+
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Bechmarks for Efficient Exploration of Completion of Multi-stage Tasks and Usage of Environmental Factors
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jansenkeith501/CS295-MADDPG
Multi-Agent Actor-Critic for Mixed 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/jingdic/rgmcomm
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/jjbong/strangeness_exploration
Value-Decomposition Networks For Cooperative Multi-Agent Learning
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/jugg1er/air
Value-Decomposition Networks For Cooperative Multi-Agent Learning
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/kcorder/qmix_variants
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/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/lachisis/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/ling-pan/res
https://github.com/madhur-tandon/RL-Project
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/madras-simulator/Multi-Agent-Particle-Environment
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/mahi97/XQMIX
https://github.com/marwanihab/RL_Tag_Game
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/marwanihab/RL_Testing_Noise_ASRN
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/matteokarldonati/Counterfactual-Multi-Agent-Policy-Gradients
https://github.com/mauricemager/multiagent-robot
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/mlr-org/mlr
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
https://github.com/morning9393/HAPPO-HATRPO
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/nju-rl/acorm
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/openai/maddpg
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/opendilab/DI-engine/blob/main/ding/policy/coma.py
https://github.com/opendilab/DI-engine/blob/main/ding/policy/qmix.py
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/opendilab/DI-engine/blob/main/ding/policy/qtran.py
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
https://github.com/osilab-kaist/smac_plus
https://github.com/oxwhirl/facmac
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.