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codes for the paper "POMO: Policy Optimization with Multiple Optima for Reinforcement Learning"
| Date | Stars |
|---|---|
| 2026-07-31 | 266 |
| 2026-08-01 | 266 |
| 2026-08-02 | 266 |
| 2026-08-06 | 266 |
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# POMO ### Files in "OLD_ipynb_ver" folders They are the original codes (2020) used for the paper<br> > POMO: Policy Optimization with Multiple Optima for Reinforcement Learning<br> > accepted at NeurIPS 2020<br> http://arxiv.org/abs/2010.16011 https://proceedings.neurips.cc/paper/2020/hash/f231f2107df69eab0a3862d50018a9b2-Abstract.html They are based on ipynb files and easier to play with interactively. <br> <br> <br> ### Files in "NEW_py_ver" folders They are the updated codes, newly structured in 2021. <br> They are based on py files, so that they can be run on servers more easily.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7b5dcb7a05914bb6, llm:Repository contains code for the paper 'POMO: Policy Optimization with Multiple Optima for Reinforcement Learning' (reinforcement learning algorithm implementation).
matched fp:7b5dcb7a05914bb6, llm:Repository contains code for the paper 'POMO: Policy Optimization with Multiple Optima for Reinforcement Learning' (reinforcement learning algorithm implementation).
matched fp:7b5dcb7a05914bb6, llm:Repository contains code for the paper 'POMO: Policy Optimization with Multiple Optima for Reinforcement Learning' (reinforcement learning algorithm implementation).