Reinforced Evolutionary Neural Architecture Search

Neural architecture search (NAS) is an important yet challenging task in network design due to its high computational consumption and low stability. To address these two issues, we propose the Reinforced Evolutionary Neural Architecture Search (RENAS), which is an evolutionary method with reinforced mutation for NAS. Our method integrates reinforced mutation into an evolution algorithm for neural architecture exploration, in which a mutation controller to learn the effects of slight modifications and make mutation actions. The reinforced mutation controller instructs the model population to evolve efficiently in a suitable direction. Furthermore, as child models can inherit parameters from their parents during evolution, our method requires very limited computational resources. We conduct the proposed search method on CIFAR-10 with 4 GPUs (Titan Xp) across 1.5 days and discover a powerful network architecture. This architecture achieves a competitive result on CIFAR-10. We further apply the explored network architecture to the mobile setting ImageNet. The network achieves a new state-of-the-art accuracy, i.e., 75.7\% top-1 accuracy with 5.36M parameters.

Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making

Tactical driving decision making is crucial for autonomous driving systems and has attracted considerable interest in recent years. In this paper, we propose several practical components that can speed up deep reinforcement learning algorithms towards tactical decision making tasks: 1) non-uniform action skipping as a more stable alternative to action-repetition frame skipping, 2) a counter-based penalty for lanes on which ego vehicle has less right-of-road, and 3) heuristic inference-time action masking for apparently undesirable actions. We evaluate the proposed components in a realistic driving simulator and compare them with several baselines. Results show that the proposed scheme provides superior performance in terms of safety, efficiency, and comfort.