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Recently, I have completed Atari Breakout (https://arxiv.org/pdf/1312.5602.pdf) with DQN.

Similar to DQN, what are the most common deep reinforcement learning algorithms and models in 2020? It seems that DQN is outdated and policy gradients are preferred.

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There are several common deep reinforcement algorithms and models apart from deep Q networks (or deep Q learning). I will list some of them below (along with a link to the paper that introduced them), but note that some of these may not be state-of-the-art (at least, not anymore, and it's likely that all of these will be replaced in the future).

For an exhaustive overview of deep RL algorithms and models, maybe take a look at this pre-print Deep Reinforcement Learning (2018) by Yuxi Li.

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