- Keywords: Deep reinforcement learning, Generalization in visual domains
- TL;DR: We propose a simple randomization technique for improving generalization in deep reinforcement learning across tasks with various unseen visual patterns.
- Abstract: Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained ones), 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 which are invariant across varied and randomized input observations. Furthermore, we propose an inference method based on Monte Carlo approximation to reduce the variance induced by this randomization. The proposed method significantly outperforms conventional techniques including various regularization and data augmentation techniques across 2D CoinRun, 3D DeepMind Lab exploration, and 3D robotics control tasks.