  Autonomous navigation in crowded environments is an open problem with many
applications, essential for the coexistence of robots and humans in the smart
cities of the future. In recent years, deep reinforcement learning approaches
have proven to outperform model-based algorithms. Nevertheless, even though the
results provided are promising, the works are not able to take advantage of the
capabilities that their models offer. They usually get trapped in local optima
in the training process, that prevent them from learning the optimal policy.
They are not able to visit and interact with every possible state
appropriately, such as with the states near the goal or near the dynamic
obstacles. In this work, we propose using intrinsic rewards to balance between
exploration and exploitation and explore depending on the uncertainty of the
states instead of on the time the agent has been trained, encouraging the agent
to get more curious about unknown states. We explain the benefits of the
approach and compare it with other exploration algorithms that may be used for
crowd navigation. Many simulation experiments are performed modifying several
algorithms of the state-of-the-art, showing that the use of intrinsic rewards
makes the robot learn faster and reach higher rewards and success rates (fewer
collisions) in shorter navigation times, outperforming the state-of-the-art.
