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# 11. Proximal Algorithms — Topics in Signal Processing
[https://www.indigits.com/tisp/proximal_operator/chapter.html](https://www.indigits.com/tisp/proximal_operator/chapter.html)
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[paper](https://arxiv.org/pdf/1707.06347)
## TL;DR
- **I read this because.. :** 배경지식 차
- **task :** RL
- **problem :** q-learning은 너무 불안정하고, trpo 는 상대적으로 복잡. data efficient하고 sclable한 arch…
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## 一言でいうと
Policy gradientは様々なタスクで利用されているが、戦略の更新幅の設定が難しく、小さいと収束が遅くなり大きいと学習が破綻する問題があった。そこで、TRPOという更新前後の戦略分布の距離を制約にするモデルをベースに、より計算を簡略化したPPOという手法を開発した。
### 論文リンク
https://openai-public.s3-us-west-…
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## 0. Article Information and Links
- Paper's project website: https://openai.com/blog/openai-baselines-ppo/
- Release date: YYYY/MM/DD
- Number of citations (as of 2020/MM/DD):
## 1. What do…
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https://arxiv.org/pdf/2401.13125.pdf
https://uq-berlin.slack.com/archives/D0168AT80RY/p1706626173108139
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[Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347)
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Where can I find the implementation details that differentiate the PPO2 algorithm from the original version reported in Proximal Policy Optimization Algorithms by Schulman?
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In some cases we may wish to rechunk our data prior to execution. This can help to balance between high scheduling overheads (too many tasks) and poor load balancing (too few tasks).
It appears …
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# Reference
- 07/2017 [Proximal policy optimization algorithms](https://arxiv.org/abs/1707.06347)
# Brief
- 基于策略梯度(PG，Policy Gradient)
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For the [Stochastic Project](https://github.com/epapoutsellis/StochasticCIL/tree/svrg), I implemented a new base class called `PGA` (Proximal Gradient Algorithm). This is a base class used for the `GD…