A system comprising: a storage device interface to collect status data from a storage device; and a policy learning agent configured to: encode collected status data into storage device states, and apply a reinforcement learning process to train an active-learning failure policy on the storage device states, the active-learning failure policy containing state-action pairs, each pair based on a probability of choosing an action from a set of actions for a given storage device state, the set of actions including to initiate a failure mitigation procedure on the storage device or to take no action, wherein the reinforcement learning process is to: monitor what actions the active-learning failure policy chooses in response to the storage device states, and reward the active-learning failure policy according to a timeliness of choosing to initiate the failure mitigation procedure relative to a failure of the storage device wherein, the reward according to the timeliness is an assigned reward value for the reward that is proportional to the time of initiating the failure mitigation procedure and a future failure of the storage device, wherein: a maximum reward value is assigned to the reward when the failure mitigation procedure is initiated a predetermined period prior to the future failure of the storage device; and a negative reward value is assigned to the reward in the event of one of the following: the failure mitigation procedure is initiated after the failure of the storage device; the failure mitigation procedure is initiated after the predetermined time period prior to failure of the storage device; or the failure mitigation procedure is initiated on the storage device that does not experience a subsequent failure. a storage device interface to collect status data from a storage device; and a policy learning agent configured to: encode collected status data into storage device states, and apply a reinforcement learning process to train an active-learning failure policy on the storage device states, the active-learning failure policy containing state-action pairs, each pair based on a probability of choosing an action from a set of actions for a given storage device state, the set of actions including to initiate a failure mitigation procedure on the storage device or to take no action, encode collected status data into storage device states, and apply a reinforcement learning process to train an active-learning failure policy on the storage device states, the active-learning failure policy containing state-action pairs, each pair based on a probability of choosing an action from a set of actions for a given storage device state, the set of actions including to initiate a failure mitigation procedure on the storage device or to take no action, wherein the reinforcement learning process is to: monitor what actions the active-learning failure policy chooses in response to the storage device states, and reward the active-learning failure policy according to a timeliness of choosing to initiate the failure mitigation procedure relative to a failure of the storage device monitor what actions the active-learning failure policy chooses in response to the storage device states, and reward the active-learning failure policy according to a timeliness of choosing to initiate the failure mitigation procedure relative to a failure of the storage device a maximum reward value is assigned to the reward when the failure mitigation procedure is initiated a predetermined period prior to the future failure of the storage device; and a negative reward value is assigned to the reward in the event of one of the following: the failure mitigation procedure is initiated after the failure of the storage device; the failure mitigation procedure is initiated after the predetermined time period prior to failure of the storage device; or the failure mitigation procedure is initiated on the storage device that does not experience a subsequent failure.