  In this paper we target the problem of transferring policies across multiple
environments with different dynamics parameters and motor noise variations, by
introducing a framework that decouples the processes of policy learning and
system identification. Efficiently transferring learned policies to an unknown
environment with changes in dynamics configurations in the presence of motor
noise is very important for operating robots in the real world, and our work is
a novel attempt in that direction. We introduce MANGA: Method Agnostic
Neural-policy Generalization and Adaptation, that trains dynamics conditioned
policies and efficiently learns to estimate the dynamics parameters of the
environment given off-policy state-transition rollouts in the environment. Our
scheme is agnostic to the type of training method used - both reinforcement
learning (RL) and imitation learning (IL) strategies can be used. We
demonstrate the effectiveness of our approach by experimenting with four
different MuJoCo agents and comparing against previously proposed transfer
baselines.
