  We introduce a novel implementation of a reinforcement learning (RL)
algorithm which is designed to find an optimal jet grooming strategy, a
critical tool for collider experiments. The RL agent is trained with a reward
function constructed to optimize the resulting 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. These results are accessible through the corresponding
GroomRL framework.
