diff --git a/units/en/unit2/hands-on.mdx b/units/en/unit2/hands-on.mdx index 6661341..7207c79 100644 --- a/units/en/unit2/hands-on.mdx +++ b/units/en/unit2/hands-on.mdx @@ -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.** Q function -- 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.** Link value policy diff --git a/units/en/unit2/q-learning.mdx b/units/en/unit2/q-learning.mdx index ec32172..5f46722 100644 --- a/units/en/unit2/q-learning.mdx +++ b/units/en/unit2/q-learning.mdx @@ -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.**