  Transfer in reinforcement learning is usually achieved through generalisation
across tasks. Whilst many studies have investigated transferring knowledge when
the reward function changes, they have assumed that the dynamics of the
environments remain consistent. Many real-world RL problems require transfer
among environments with different dynamics. To address this problem, we propose
an approach based on successor features in which we model successor feature
functions with Gaussian Processes permitting the source successor features to
be treated as noisy measurements of the target successor feature function. Our
theoretical analysis proves the convergence of this approach as well as the
bounded error on modelling successor feature functions with Gaussian Processes
in environments with both different dynamics and rewards. We demonstrate our
method on benchmark datasets and show that it outperforms current baselines.
