Evolutionary Computation for Reinforcement Learning

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How researchers see this paper

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Deep learning in neural networks: An overview (2014, background): “In practical applications, however, current artificial networks of spiking neurons cannot yet compete with the best traditional deep NNs (e.g., compare MNIST results of Section 5.19).”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “1 RL through Direct and Indirect Search in RNN Program Space It is possible to train small RNNs with a few 100 or 1000 weights using evolutionary algorithms [200, 255, 105, 56, 68] to search the space of NN weights [165, 307, 44, 321, 180, 259, 320, 164, 173, 69, 71, 187, 121, 313, 66, 270, 269, 305”
A Survey of Multi-Objective Sequential Decision-Making (2013, background): “…research on both multi-objective evolutionary algorithms (MOEAs) (Coello Coello, Lamont, & Van Veldhuizen, 2002; Tan, Khor, Lee, & Sathikannan, 2003; Drugan & Thierens, 2012) and evolutionary methods for RL (Whiteson, 2012), there is surprisingly little work on evolutionary approaches to MORL.”
Policy Optimization by Genetic Distillation (2017, unknown): “We provide a brief summary; a detailed survey is provided by Whiteson (2012).”