Reinforcement learning algorithms often struggle to learn complex behaviors due to the exploration-exploitation dilemma. A novel method called "Penalize with Slots" proposes a solution by introducing a penalty mechanism based on a set of slots. These slots represent important aspects of the learner's behavior, and the agent is penalized when its ac