-
https://arxiv.org/abs/1611.01626
-
Dear Mr.hongzi
I was interested in your resource scheduling method. Now, I stuck in your network class. I can't understand why you used the blow function:
`loss = T.log(prob_act[T.arange(N), actions…
-
### What happened + What you expected to happen
I assume the error is related to the action space being large because I can not reproduce it when the action space is much smaller (ie 10 times fewer…
AJSVB updated
1 month ago
-
[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…
-
- https://arxiv.org/abs/1609.05473
- 2017 AAAI
識別モデルを用いて生成モデルを学習するGenerative Adversarial Nets (GAN)は、生成モデルを学習する新しい方法として、実値データの生成に大きな成功を収めている。
しかし、離散的なトークンのシーケンスを生成することを目的とした場合には、限界があります。
その主な理由は…
e4exp updated
2 years ago
-
## Abstract
#### Problem
- GAN has considerable success in generating real-valued data. **However, it has limitations when the goal is for generating sequences of discrete tokens.**
1. the discre…
hon9g updated
5 years ago
-
I am a little confused in the implementation of spinningup's SAC. In spinningup's tutorial, SAC's runs a policy ascent to maximize the (Q(a) + log(p(a))), but when I read the code, I find that the co…
-
https://arxiv.org/abs/1611.01626
-
Got this error when initially ran the policy_gradient. example script.
![metadata_issue](https://github.com/osinenkop/regelum-control/assets/161316313/4a899de9-a9e7-4baa-9969-7ad3c7f0b211)
a1mrz updated
5 months ago
-