  Recent advances in reinforcement learning have demonstrated its ability to
solve hard agent-environment interaction tasks on a super-human level. However,
the application of reinforcement learning methods to practical and real-world
tasks is currently limited due to most RL state-of-art algorithms' sample
inefficiency, i.e., the need for a vast number of training episodes. For
example, OpenAI Five algorithm that has beaten human players in Dota 2 has
trained for thousands of years of game time. Several approaches exist that
tackle the issue of sample inefficiency, that either offers a more efficient
usage of already gathered experience or aim to gain a more relevant and diverse
experience via a better exploration of an environment. However, to our
knowledge, no such approach exists for model-based algorithms, that showed
their high sample efficiency in solving hard control tasks with
high-dimensional state space. This work connects exploration techniques and
model-based reinforcement learning. We have designed a novel exploration method
that takes into account features of the model-based approach. We also
demonstrate through experiments that our method significantly improves the
performance of the model-based algorithm Dreamer.
