  Vision-based reinforcement learning (RL) is a promising technique to solve
control tasks involving images as the main observation. State-of-the-art RL
algorithms still struggle in terms of sample efficiency, especially when using
image observations. This has led to an increased attention on integrating state
representation learning (SRL) techniques into the RL pipeline. Work in this
field demonstrates a substantial improvement in sample efficiency among other
benefits. However, to take full advantage of this paradigm, the quality of
samples used for training plays a crucial role. More importantly, the diversity
of these samples could affect the sample efficiency of vision-based RL, but
also its generalization capability. In this work, we present an approach to
improve the sample diversity. Our method enhances the exploration capability of
the RL algorithms by taking advantage of the SRL setup. Our experiments show
that the presented approach outperforms the baseline for all tested
environments. These results are most apparent for environments where the
baseline method struggles. Even in simple environments, our method stabilizes
the training, reduces the reward variance and boosts sample efficiency.
