UC Berkeley researchers open-source RAD to improve any reinforcement learning algorithm

In an accompanying paper, the authors say this module can improve any existing reinforcement learning algorithm and that RAD achieves better compute and data efficiency than Google AI's PlaNet, as well as recently released cutting-edge algorithms like DeepMind's Dreamer and SLAC from UC Berkeley and DeepMind. RAD achieves state-of-the-art results on common benchmarks and matches or beats every baseline in terms of performance and data efficiency across 15 DeepMind control environments, the researchers say. It does this in part by applying data augmentations for visual observations. Coauthors of the paper on RAD include Michael "Misha" Laskin, Kimin Lee, and Berkeley AI Research codirector and Covariant founder Pieter Abbeel. RAD was released Thursday on preprint repository arXiv.