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