  While model-based deep reinforcement learning (RL) holds great promise for
sample efficiency and generalization, learning an accurate dynamics model is
often challenging and requires substantial interaction with the environment. A
wide variety of domains have dynamics that share common foundations like the
laws of classical mechanics, which are rarely exploited by existing algorithms.
In fact, humans continuously acquire and use such dynamics priors to easily
adapt to operating in new environments. In this work, we propose an approach to
learn task-agnostic dynamics priors from videos and incorporate them into an RL
agent. Our method involves pre-training a frame predictor on task-agnostic
physics videos to initialize dynamics models (and fine-tune them) for unseen
target environments. Our frame prediction architecture, SpatialNet, is designed
specifically to capture localized physical phenomena and interactions. Our
approach allows for both faster policy learning and convergence to better
policies, outperforming competitive approaches on several different
environments. We also demonstrate that incorporating this prior allows for more
effective transfer between environments.
