I was surprised to see this loss function because it is generally used when the target is a distribution (i.e. sums to 1). This is not the case for the advantage estimate. However, I worked out the math and it does appear to be doing the right thing which is neat!

I think this trick should be mentioned in the code.

• I have marked all applicable categories:
  • exception-raising bug
  • RL algorithm bug
  • documentation request (i.e. "X is missing from the documentation.")
  • new feature request
• I have visited the [source website], and in particular read the [known issues]
• I have searched through the [issue tracker] for duplicates
• I have mentioned versio

BTgym have two main sections, the Gym framework and the RL algorithm framework.
The RL part is tailored to the unique gym requirements of BTgym, but as new research in the field is emerging there will be a benefit in exploring new algorithms that aren't implemented by this project.

The following tutorial is my own attempt of testing the integration between the Gym part of BTgym with an externa

1-grid-word ---> 1-policy-iteration 에서

코드 전제적으로 width, height 순서가 맞지 않습니다.
코드에서는 widht=5, height=5로 되어 있어, 작동하지만,
width=5, height=6이면, 작동하지 않습니다.

예들 들어,

self.value_table = [[0.0] * env.width for _ in range(env.height)]  # height x width

--->

self.value_table = [[0.0] * env.height for _ in range(env.width)]  # width x height

코드 전체를 좀 손봐야 할 것 같습니다.

graphic상 에

Paper: https://arxiv.org/abs/1801.01290
Blog Post: https://bair.berkeley.edu/blog/2018/12/14/sac/