vault backup: 2026-06-04 19:49:35

Running Start/CSB320 - Machine Learning Concepts/Class 6-4 (Reinforcement Learning).md

@@ -36,4 +36,6 @@
 		- This is what prioritizes future rewards vs immediate rewards
 		- future rewards (the $max$ part) are deprioritized in relation to immediate rewards
 	- $maxQ(s_{t+1}, a)$ is the best q value from the next state (future reward)
-		- this is what backpropagates future rewards 
+		- this is what backpropagates future rewards 
+	- during exploration phase paths are usually explored randomly to attempt to find the q values for each path
+	-