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Average length of evaluation runs (with í µí¼ = 0.05) on a 25x25 Gridworld with potential-based reward shaping where Φ(í µí± ) = í µí± * (í µí± ).
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Potential-based reward shaping is commonly used to incorporate prior knowledge of how to solve the task into reinforcement learning because it can formally guarantee policy invariance. As such, the optimal policy and the ordering of policies by their returns are not altered by potential-based reward shaping. In this work, we highlight the dependenc...
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