  Besides the recent impressive results on reinforcement learning (RL), safety
is still one of the major research challenges in RL. RL is a machine-learning
approach to determine near-optimal policies in Markov decision processes
(MDPs). In this paper, we consider the setting where the safety-relevant
fragment of the MDP together with a temporal logic safety specification is
given and many safety violations can be avoided by planning ahead a short time
into the future. We propose an approach for online safety shielding of RL
agents. During runtime, the shield analyses the safety of each available
action. For any action, the shield computes the maximal probability to not
violate the safety specification within the next $k$ steps when executing this
action. Based on this probability and a given threshold, the shield decides
whether to block an action from the agent. Existing offline shielding
approaches compute exhaustively the safety of all state-action combinations
ahead of time, resulting in huge computation times and large memory
consumption. The intuition behind online shielding is to compute at runtime the
set of all states that could be reached in the near future. For each of these
states, the safety of all available actions is analysed and used for shielding
as soon as one of the considered states is reached. Our approach is well suited
for high-level planning problems where the time between decisions can be used
for safety computations and it is sustainable for the agent to wait until these
computations are finished. For our evaluation, we selected a 2-player version
of the classical computer game SNAKE. The game represents a high-level planning
problem that requires fast decisions and the multiplayer setting induces a
large state space, which is computationally expensive to analyse exhaustively.
