  We introduce a Bayesian (deep) model-based reinforcement learning method
(RoMBRL) that can capture model uncertainty to achieve sample-efficient policy
optimisation. We propose to formulate the model-based policy optimisation
problem as a Bayes-adaptive Markov decision process (BAMDP). RoMBRL maintains
model uncertainty via belief distributions through a deep Bayesian neural
network whose samples are generated via stochastic gradient Hamiltonian Monte
Carlo. Uncertainty is propagated through simulations controlled by sampled
models and history-based policies. As beliefs are encoded in visited histories,
we propose a history-based policy network that can be end-to-end trained to
generalise across history space and will be trained using recurrent
Trust-Region Policy Optimisation. We show that RoMBRL outperforms existing
approaches on many challenging control benchmark tasks in terms of sample
complexity and task performance. The source code of this paper is also publicly
available on https://github.com/thobotics/RoMBRL.
