  In the last decade, reinforcement learning successfully solved complex
control tasks and decision-making problems, like the Go board game. Yet, there
are few success stories when it comes to deploying those algorithms to
real-world scenarios. One of the reasons is the lack of guarantees when dealing
with and avoiding unsafe states, a fundamental requirement in critical control
engineering systems. In this paper, we introduce Guided Safe Shooting (GuSS), a
model-based RL approach that can learn to control systems with minimal
violations of the safety constraints. The model is learned on the data
collected during the operation of the system in an iterated batch fashion, and
is then used to plan for the best action to perform at each time step. We
propose three different safe planners, one based on a simple random shooting
strategy and two based on MAP-Elites, a more advanced divergent-search
algorithm. Experiments show that these planners help the learning agent avoid
unsafe situations while maximally exploring the state space, a necessary aspect
when learning an accurate model of the system. Furthermore, compared to
model-free approaches, learning a model allows GuSS reducing the number of
interactions with the real-system while still reaching high rewards, a
fundamental requirement when handling engineering systems.
