CS_ALearn/deep-rl-class
1
0
Fork 0
mirror of https://github.com/huggingface/deep-rl-class.git synced 2026-04-13 17:29:52 +08:00
Code Issues Projects Releases Wiki Activity

Small updates Unit 2

Browse Source
This commit is contained in:
simoninithomas
2022-12-12 03:57:37 +01:00
parent 5c8432379e
commit 7b9c1cf0a4
1 changed files with 1 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
units/en/unit2/bellman-equation.mdx
View File

@@ -41,7 +41,7 @@ If we go back to our example, we can say that the value of State 1 is equal to t
To calculate the value of State 1: the sum of rewards **if the agent started in that state 1** and then followed the **policy for all the time steps.**
This is equivalent to \\(V(S_{t})\\) = Immediate reward \\(R_{t+1}\\) + Discounted value of the next state \\(gamma * V(S_{t+1})\\)
This is equivalent to \\(V(S_{t})\\) = Immediate reward \\(R_{t+1}\\) + Discounted value of the next state \\(\gamma * V(S_{t+1})\\)
<figure>
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/bellman6.jpg" alt="Bellman equation"/>