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In the article Playing Atari with Deep Reinforcement Learning, Mnih et al, 2013, which was a major outbreak in Deep Reinforcement learning (especially in Deep Q learning), they don't feed only the last image to the network. They stack the 4 last images : For the experiments in this paper, the function φ from algorithm 1 applies this preprocessing to the ...


1

Replacement you suggest is replacement of random variable by its expectation in forward part of TD. It would make IQN into modification of C51 with randomly sampled function approximator instead of discrete distribution. Both distribution produced and especially exploration behavior with your replacement would be very different. The authors of paper ...


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