https://bitbucket.org/skeizer/madrigal
Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue Modelling
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/15534081591/QMIX
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/16444take/aope-sim
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
https://github.com/2023-MindSpore-1/ms-code-211/tree/main/DecoMR
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning
https://github.com/2023-MindSpore-1/ms-code-221/tree/main/qmix
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/Abdelhamid-bouzid/Multi-Agent-Deep-Deterministic-Policy-Gradient
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/Ah31/maddpg_pytorch
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/AleXander-Tsui/MPE
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/AstraZeneca/awesome-shapley-value
https://github.com/BNN-UPC/MARL-GNN-TE
Is Machine Learning Ready for Traffic Engineering Optimization?
https://github.com/Branbados/rlcard
https://github.com/Chan1998/MAAC
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/Denys88/rl_games
https://github.com/Dikshuy/SMARTS-lite
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
https://github.com/Duckkkky/smarts
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
https://github.com/Epsilon10/MARL
Reward Machines for Cooperative Multi-Agent Reinforcement Learning
https://github.com/EyaRhouma/collaboration-competition-MADDPG
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/HanbumKo/distributed-wqmix
https://github.com/HassamSheikh/VIP_Protection_Envs
https://github.com/HumanCompatibleAI/multi-agent
A multi-agent reinforcement learning model of common-pool resource appropriation
https://github.com/IngookJang/reinforcement-learning-TX2
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence
https://github.com/JacopoPan/gym-pybullet-drones
https://github.com/JinTanda/MADDPG_env
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/JohannesAck/tf2multiagentrl
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/LXYYY/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/Louiii/ValueDecomposition
Value-Decomposition Networks For Cooperative Multi-Agent Learning
https://github.com/MLStruckmann/mutation-misery
On the Performance of Differential Evolution for Hyperparameter Tuning
https://github.com/MUmarJaved/MultiAgent-Distributed-Reinforcement-Learning
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
https://github.com/MindSpore-paper-code-3/code2/tree/main/DecoMR
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning
https://github.com/MindSpore-paper-code-3/code8/tree/main/DecoMR
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning
https://github.com/MrDaubinet/collaboration-and-competition
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/Multi-Agent-Research-Group/SQDDPG
Shapley Q-value: A Local Reward Approach to Solve Global Reward Games
https://github.com/NeuroCSUT/intentions
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/PKU-AI-Edge/DGN/
CoLight: Learning Network-level Cooperation for Traffic Signal Control
https://github.com/PKU-Alignment/ProAgent
https://github.com/PettingZoo-Team/PettingZoo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.