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Phylogeny-Informed Interaction Estimation Accelerates Co-Evolutionary Learning
Co-evolution is a powerful problem-solving approach.
Emergent Resource Exchange and Tolerated Theft Behavior using Multi-Agent Reinforcement Learning
For decades, the evolution of cooperation has piqued the interest of numerous academic disciplines such as game theory, economics, biology, and computer science.
Playing Nondeterministic Games through Planning with a Learned Model
The MuZero algorithm is known for achieving high-level performance on traditional zero-sum two-player games of perfect information such as chess, Go, and shogi, as well as visual, non-zero sum, single-player environments such as the Atari suite.
Coevolutionary Neural Population Models
We present a method for using neural networks to model evolutionary population dynamics, and draw parallels to recent deep learning advancements in which adversarially-trained neural networks engage in coevolutionary interactions.
