  It is vital to learn effective policies that can be transferred to different
domains with dynamics discrepancies in reinforcement learning (RL). In this
paper, we consider dynamics adaptation settings where there exists dynamics
mismatch between the source domain and the target domain, and one can get
access to sufficient source domain data, while can only have limited
interactions with the target domain. Existing methods address this problem by
learning domain classifiers, performing data filtering from a value discrepancy
perspective, etc. Instead, we tackle this challenge from a decoupled
representation learning perspective. We perform representation learning only in
the target domain and measure the representation deviations on the transitions
from the source domain, which we show can be a signal of dynamics mismatch. We
also show that representation deviation upper bounds performance difference of
a given policy in the source domain and target domain, which motivates us to
adopt representation deviation as a reward penalty. The produced
representations are not involved in either policy or value function, but only
serve as a reward penalizer. We conduct extensive experiments on environments
with kinematic and morphology mismatch, and the results show that our method
exhibits strong performance on many tasks. Our code is publicly available at
https://github.com/dmksjfl/PAR.
