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@@ -29,6 +29,8 @@ All our PPO implementations below are augmented with the same code-level optimiz
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| :material-github: [`ppo_atari_lstm.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_lstm.py), :material-file-document: [docs](/rl-algorithms/ppo/#ppo_atari_lstmpy)| For Atari games using LSTM without stacked frames. |
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| :material-github: [`ppo_atari_envpool.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool.py), :material-file-document: [docs](/rl-algorithms/ppo/#ppo_atari_envpoolpy)| Uses the blazing fast Envpool Atari vectorized environment. |
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| :material-github: [`ppo_procgen.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_procgen.py), :material-file-document: [docs](/rl-algorithms/ppo/#ppo_procgenpy)| For the procgen environments |
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