CS_ALearn/deep-rl-class
1
0
Fork 0
mirror of https://github.com/huggingface/deep-rl-class.git synced 2026-04-14 10:22:37 +08:00
Code Issues Projects Releases Wiki Activity

Proposal on small details...

Browse Source
This commit is contained in:
PierreCounathe
2023-08-27 23:09:52 +02:00
parent eed8941ff5
commit 05188727f3
2 changed files with 4 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
units/en/unit2/hands-on.mdx
View File

@@ -93,16 +93,16 @@ Before diving into the notebook, you need to:
*Q-Learning* **is the RL algorithm that**:
- Trains *Q-Function*, an **action-value function** that encoded, in internal memory, by a *Q-table* **that contains all the state-action pair values.**
- Trains *Q-Function*, an **action-value function** that is encoded, in internal memory, by a *Q-table* **that contains all the state-action pair values.**
- Given a state and action, our Q-Function **will search the Q-table for the corresponding value.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/Q-function-2.jpg" alt="Q function" width="100%"/>
- When the training is done,**we have an optimal Q-Function, so an optimal Q-Table.**
- When the training is done, **we have an optimal Q-Function, so an optimal Q-Table.**
- And if we **have an optimal Q-function**, we
have an optimal policy, since we **know for, each state, the best action to take.**
have an optimal policy, since we **know for each state, the best action to take.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/link-value-policy.jpg" alt="Link value policy" width="100%"/>

2
units/en/unit2/q-learning.mdx
View File

@@ -113,7 +113,7 @@ This means that to update our \\(Q(S_t, A_t)\\):
- To update our Q-value at a given state-action pair, we use the TD target.
How do we form the TD target?
1. We obtain the reward after taking the action \\(R_{t+1}\\).
1. We obtain the reward \\(R_{t+1}\\) after taking the action \\(A_t\\).
2. To get the **best state-action pair value** for the next state, we use a greedy policy to select the next best action. Note that this is not an epsilon-greedy policy, this will always take the action with the highest state-action value.
Then when the update of this Q-value is done, we start in a new state and select our action **using a epsilon-greedy policy again.**