Three NeurIPS papers on learning, foraging, and evolution

The papers are entitled:
1. Payoff-Only Learning in Games via Best-Response Differential Inclusions. Ismail Hassan, Pedro G. Lind and Anis Yazidi 

2. Evolutionary foraging in grids: Intermittent search emerges as an optimal strategy. Shailendra Bhandari, Alex Szorkovszky, Anis Yazidi and Pedro G. Lind

3. When evolution cheats: Frozen-weights Baselines reveal static-solvers interference in evolved plastic spiking neural networks. Denis Larsen, Kazi Shah Nawaz Ripon, Anis Yazidi and Gustavo Borges Moreno e Mello

Inspired by biological and natural processes, the three accepted papers explore how adaptive strategies emerge in complex environments and examine the complementary roles of evolution and learning in When evolution cheats. They investigate how Nash equilibria emerge through reinforcement learning in Payoff-Only Learning in Games and how evolution can rediscover natural laws, such as optimal foraging strategies, in Evolutionary foraging in grids.