Making failure scenarios using adversarial reinforcement learning is performed by storing, in a first storage, a variety of first experiences of failures of a player agent due to an adversarial agent, and performing a simulation of an environment including the player agent and the adversarial agent. It also includes calculating a similarity of a second experience of a failure of the player agent in the simulation and each of the variety of first experiences in the first storage, and updating the first storage by adding the second experience as a new first experience of the variety of first experiences in response to the similarity being less than a threshold. Additionally, the use of adversarial reinforcement learning can include training the adversarial agent by using at least one of the plurality of first experiences in the first storage to generate an adversarial agent having diverse experiences.
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