  Reinforcement learning (RL) agents need to explore their environments in
order to learn optimal policies. In many environments and tasks, safety is of
critical importance. The widespread use of simulators offers a number of
advantages, including safe exploration which will be inevitable in cases when
RL systems need to be trained directly in the physical environment (e.g. in
human-robot interaction). The popular Safety Gym library offers three mobile
agent types that can learn goal-directed tasks while considering various safety
constraints. In this paper, we extend the applicability of safe RL algorithms
by creating a customized environment with Panda robotic arm where Safety Gym
algorithms can be tested. We performed pilot experiments with the popular PPO
algorithm comparing the baseline with the constrained version and show that the
constrained version is able to learn the equally good policy while better
complying with safety constraints and taking longer training time as expected.
