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Effects of sparse rewards of different magnitudes in the speed of learning of model-based actor critic methods
Actor critic methods with sparse rewards in model-based deep reinforcement learning typically require a deterministic binary reward function that reflects only two possible outcomes: if, for each step, the goal has been achieved or not.
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