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Traditional model-based RL relies on hand-specified or learned models oftransition dynamics of the environment. These methods are sample efficient andfacilitate learning in the real world but fail to generalize to subtlevariations in the underlying dynamics, e.g., due to differences in mass,friction, or actuators across robotic agents or across time. We propose usingvariational inference to learn an explicit latent representation of unknownenvironment properties that accelerates learning and facilitates generalizationon novel environments at test time. We use Online Bayesian Inference of theselearned latents to rapidly adapt online to changes in environments withoutretaining large replay buffers of recent data. Combined with a neural networkensemble that models dynamics and captures uncertainty over dynamics, ourapproach demonstrates positive transfer during training and online adaptationon the continuous control task HalfCheetah.

Efficient transfer learning and online adaptation with latent variable models for continuous control