  Adapting an agent's behaviour to new environments has been one of the primary
focus areas of physics based reinforcement learning. Although recent approaches
such as universal policy networks partially address this issue by enabling the
storage of multiple policies trained in simulation on a wide range of
dynamic/latent factors, efficiently identifying the most appropriate policy for
a given environment remains a challenge. In this work, we propose a Gaussian
Process-based prior learned in simulation, that captures the likely performance
of a policy when transferred to a previously unseen environment. We integrate
this prior with a Bayesian Optimisation-based policy search process to improve
the efficiency of identifying the most appropriate policy from the universal
policy network. We empirically evaluate our approach in a range of continuous
and discrete control environments, and show that it outperforms other competing
baselines.
