Pinned Repositories
EDTI-EITI
NDQ
CASEC-MACO-benchmark
Codes accompanying the paper "Context-Aware Sparse Deep Coordination Graphs (https://arxiv.org/abs/2106.02886).
DOP
Codes accompanying the paper "DOP: Off-Policy Multi-Agent Decomposed Policy Gradients" (ICLR 2021, https://arxiv.org/abs/2007.12322)
EITI-EDTI
Codes accompanying the paper "Influence-Based Multi-Agent Exploration" (ICLR 2020 spotlight)
Homophily-MARL
Code for "A Closer Look at Cooperation Emergence via Multi-Agent RL"
NDQ
Codes accompanying the paper "Learning Nearly Decomposable Value Functions with Communication Minimization" (ICLR 2020)
RODE
Codes accompanying the paper "RODE: Learning Roles to Decompose Multi-Agent Tasks (ICLR 2021, https://arxiv.org/abs/2010.01523). RODE is a scalable role-based multi-agent learning method which effectively discovers roles based on joint action space decomposition according to action effects, establishing a new state of the art on the StarCraft multi-agent benchmark.
ROMA
Codes accompanying the paper "ROMA: Multi-Agent Reinforcement Learning with Emergent Roles" (ICML 2020 https://arxiv.org/abs/2003.08039)
tonghanwang.github.io
TonghanWang's Repositories
TonghanWang/ROMA
Codes accompanying the paper "ROMA: Multi-Agent Reinforcement Learning with Emergent Roles" (ICML 2020 https://arxiv.org/abs/2003.08039)
TonghanWang/NDQ
Codes accompanying the paper "Learning Nearly Decomposable Value Functions with Communication Minimization" (ICLR 2020)
TonghanWang/RODE
Codes accompanying the paper "RODE: Learning Roles to Decompose Multi-Agent Tasks (ICLR 2021, https://arxiv.org/abs/2010.01523). RODE is a scalable role-based multi-agent learning method which effectively discovers roles based on joint action space decomposition according to action effects, establishing a new state of the art on the StarCraft multi-agent benchmark.
TonghanWang/DOP
Codes accompanying the paper "DOP: Off-Policy Multi-Agent Decomposed Policy Gradients" (ICLR 2021, https://arxiv.org/abs/2007.12322)
TonghanWang/EITI-EDTI
Codes accompanying the paper "Influence-Based Multi-Agent Exploration" (ICLR 2020 spotlight)
TonghanWang/CASEC-MACO-benchmark
Codes accompanying the paper "Context-Aware Sparse Deep Coordination Graphs (https://arxiv.org/abs/2106.02886).
TonghanWang/Homophily-MARL
Code for "A Closer Look at Cooperation Emergence via Multi-Agent RL"
TonghanWang/tonghanwang.github.io