  One's ability to learn a generative model of the world without supervision
depends on the extent to which one can construct abstract knowledge
representations that generalize across experiences. To this end, capturing an
accurate statistical structure from observational data provides useful
inductive biases that can be transferred to novel environments. Here, we tackle
the problem of learning to control dynamical systems by applying Bayesian
nonparametric methods, which is applied to solve visual servoing tasks. This is
accomplished by first learning a state space representation, then inferring
environmental dynamics and improving the policies through imagined future
trajectories. Bayesian nonparametric models provide automatic model adaptation,
which not only combats underfitting and overfitting, but also allows the
model's unbounded dimension to be both flexible and computationally tractable.
By employing Gaussian processes to discover latent world dynamics, we mitigate
common data efficiency issues observed in reinforcement learning and avoid
introducing explicit model bias by describing the system's dynamics. Our
algorithm jointly learns a world model and policy by optimizing a variational
lower bound of a log-likelihood with respect to the expected free energy
minimization objective function. Finally, we compare the performance of our
model with the state-of-the-art alternatives for continuous control tasks in
simulated environments.
