NAS Reading List

NAS

[x] （ICLR2017, google brain）Neural architecture search with reinforcement learning
[ ] （2019.11）Meta-Learning of Neural Architectures for Few-Shot Learning，meta与NAS的结合：https://arxiv.org/abs/1911.11090v1
[x] （2019.01）Designing neural networks through neuroevolution，NE方法综述
（2017.09）Evolution Strategies as a Scalable Alternative to Reinforcement Learning https://openai.com/blog/evolution-strategies/， https://arxiv.org/abs/1703.03864：NES方法与DQN、A3C相匹敌（但未完全脱离梯度）
（2018.04）Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning https://arxiv.org/abs/1712.06567：gradient-free的NE方法与DQN、A3C相匹敌
（2018.04）Simple random search provides a competitive approach to reinforcement learning https://arxiv.org/abs/1803.07055：简化NE方法（RS方法）与RPO、PPO、DDPG相匹敌

（2018.05）Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients https://arxiv.org/abs/1712.06563：保存状态与动作之间的关系库
（ICLR 2018.05）Policy Optimization by Genetic Distillation https://arxiv.org/abs/1711.01012：Genetic policy optimization
（ICLR 2018）Noisy Networks for Exploration https://arxiv.org/abs/1706.10295
（ICLR 2018）Parameter space noise for exploration https://arxiv.org/abs/1706.01905

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities https://arxiv.org/abs/1803.03453 ：综述
（NIPS 2018）Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents https://arxiv.org/abs/1712.06560
（NIPS workshop 2018）Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems https://arxiv.org/abs/1806.00553

From Nodes to Networks: Evolving Recurrent Neural Networks https://arxiv.org/abs/1803.04439
（AAAI 2019）Regularized Evolution for Image Classifier Architecture Search https://arxiv.org/abs/1802.01548