Resources

• NIPS 2016 tutorial on Deep Reinforcement Learning
• Paper containing the list of functions -- Onbaşoğlu, Esin, and Linet Özdamar. "Parallel simulated annealing algorithms in global optimization." Journal of Global Optimization 19.1 (2001): 27-50.(pdf)
• Implementation of Cross Entropy in R -- Benham, Tim, et al. "CEoptim: Cross-Entropy R Package for Optimization." arXiv preprint arXiv:1503.01842 (2015).
• Tetris paper containing the most basic version of Cross Entropy -- Szita, István, and András Lörincz. "Learning Tetris using the noisy cross-entropy method." Neural computation 18.12 (2006): 2936-2941.(pdf)

Papers related to Heidelberg Hardware

• Brüderle, Daniel, et al. "A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems." Biological cybernetics 104.4-5 (2011): 263-296.
• Petrovici, Mihai A., et al. "Characterization and compensation of network-level anomalies in mixed-signal neuromorphic modeling platforms." PloS one 9.10 (2014): e108590.
• thesis from Klähn/Heidelberg -- Up-to-date information about the hardware