Before revision:  We also provide learning-based microfoundations for our cross-player tremble restrictions in a class of games using three different specifications of how agents handle the exploration/exploitation trade-off: expected utility maximization, Bayesian upper confidence bounds, and Thompson sampling. Finally, we conduct a lab experiment based on one of our examples and verify PCE leads to better predictions than other equilibrium concepts .
After revision:  We also provide learning-based microfoundations for our cross-player tremble restrictions in a steady-state setting and verify that our theoretical predictions also apply to finite-population simulations when all agents start learning at the same time .