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Generative Adversarial Imitation Learning suffers from the fundamentalproblem of reward bias stemming from the choice of reward functions used in thealgorithm. Different types of biases also affect different types ofenvironments - which are broadly divided into survival and task-basedenvironments. We provide a theoretical sketch of why existing reward functionswould fail in imitation learning scenarios in task based environments withmultiple terminal states. We also propose a new reward function for GAIL whichoutperforms existing GAIL methods on task based environments with single andmultiple terminal states and effectively overcomes both survival andtermination bias.

Addressing reward bias in Adversarial Imitation Learning with neutral reward functions