https://github.com/hepengli/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Papers with Code 아카이브
SMAC
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The StarCraft Multi-Agent Challenge (SMAC) is a benchmark that provides elements of partial observability, challenging dynamics, and high-dimensional observation spaces. SMAC is built using the StarCraft II game engine, creating a testbed for research in cooperative MARL where each game unit is an independent RL agent.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/huaifeng1993/DFANet
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
https://github.com/ifpen/wfcrl-benchmark
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
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/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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.