  In these proceedings, we present a library allowing for straightforward calls
in C++ to jet grooming algorithms trained with deep reinforcement learning. The
RL agent is trained with a reward function constructed to optimize the groomed
jet properties, using both signal and background samples in a simultaneous
multi-level training. We show that the grooming algorithm derived from the deep
RL agent can match state-of-the-art techniques used at the Large Hadron
Collider, resulting in improved mass resolution for boosted objects. Given a
suitable reward function, the agent learns how to train a policy which
optimally removes soft wide-angle radiation, allowing for a modular grooming
technique that can be applied in a wide range of contexts. The neural network
trained with GroomRL can be used in a FastJet analysis through the libGroomRL
C++ library.
