This repository provides the official implementation of the RAD paper (Laskin et al., 2020), which combines data augmentations with model-free deep reinforcement learning to improve sample efficiency. It includes implementations of SAC, PPO, and CURL agents, with configurable augmentation pipelines (crop, rotate, flip, etc.) for image-based observations. The codebase trains agents on environments from DM-Control and OpenAI Gym using PyTorch, with configurable hyperparameters via command-line arguments.