Pinned Repositories
SeCA
Codes of SeCA accompanying the paper "Sequential Cooperative Multi-Agent Reinforcement Learning"(AAMAS 2023). SeCA is a sequential credit assignment method that factorizes and simplifies the complex interaction analysis of multi-agent systems into a sequential evaluation process for more efficient learning.
GoMARL
Codes of GoMARL accompanying the paper "Automatic Grouping for Efficient Cooperative Multi-Agent Reinforcement Learning"(NeurIPS 2023). GoMARL is a domain-agnostic MARL method that learns automatic grouping for efficient cooperation by promoting intra- and inter-group coordination.
SeCA
Codes of SeCA accompanying the paper "Sequential Cooperative Multi-Agent Reinforcement Learning"(AAMAS 2023). SeCA is a sequential credit assignment method that factorizes and simplifies the complex interaction analysis of multi-agent systems into a sequential evaluation process for more efficient learning.
zyfsjycc's Repositories
zyfsjycc/GoMARL
Codes of GoMARL accompanying the paper "Automatic Grouping for Efficient Cooperative Multi-Agent Reinforcement Learning"(NeurIPS 2023). GoMARL is a domain-agnostic MARL method that learns automatic grouping for efficient cooperation by promoting intra- and inter-group coordination.
zyfsjycc/SeCA
Codes of SeCA accompanying the paper "Sequential Cooperative Multi-Agent Reinforcement Learning"(AAMAS 2023). SeCA is a sequential credit assignment method that factorizes and simplifies the complex interaction analysis of multi-agent systems into a sequential evaluation process for more efficient learning.