Deep reinforcement learning (RL) agents often fail to generalize to unseen
environments (yet semantically similar to trained agents), particularly when
they are trained on high-dimensional state spaces, such as images. In this
paper, we propose a simple technique to improve a generalization ability of
deep RL agents by introducing a randomized (convolutional) neural network that
randomly perturbs input observations. It enables trained agents to adapt to new
domains by learning robust features invariant across varied and randomized
environments. Furthermore, we consider an inference method based on the Monte
Carlo approximation to reduce the variance induced by this randomization. We
demonstrate the superiority of our method across 2D CoinRun, 3D DeepMind Lab
exploration and 3D robotics control tasks: it significantly outperforms various
regularization and data augmentation methods for the same purpose.

本文提出一种简单有效的技术，通过引入随机卷积神经网络打乱输入观测数据，在提高深度强化学习智能体的泛化能力方面取得了显著效果，并且通过 Monte Carlo 近似的推理方法来减少随机化引起的方差。我们在 2D CoinRun、3D DeepMind Lab 探索和 3D 机器人控制任务中展示了我们的方法的优越性，相比于其他正则化和数据增强方法明显更加优秀。