1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network
Wei Li, Weiyan Liu, Shitong Shao +1
Deducing the contribution of each agent and assigning the corresponding reward to them is a crucial problem in cooperative Multi-Agent Reinforcement Learning (MARL). Previous studi…
q-bio.PE2010
Learning, evolution and population dynamics
Juergen Jost, Wei Li
We study a complementarity game as a systematic tool for the investigation of the interplay between individual optimization and population effects and for the comparison of differe…
q-bio.PE2010
The tragedy of the commons in a multi-population complementarity game
Juergen Jost, Wei Li
We study a complementarity game with multiple populations whose members' offered contributions are put together towards some common aim. When the sum of the players' offers reaches…