From 3bccadc8737941f34602b13539fd66283585d376 Mon Sep 17 00:00:00 2001
From: ben-jaynes <1btjaynes@gmail.com>
Date: Thu, 4 Jun 2026 19:49:35 -0700
Subject: [PATCH] vault backup: 2026-06-04 19:49:35

---
 Excalidraw/BellmanEquation.excalidraw.md                    | 6 +++---
 .../Class 6-4 (Reinforcement Learning).md                   | 4 +++-
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/Excalidraw/BellmanEquation.excalidraw.md b/Excalidraw/BellmanEquation.excalidraw.md
index 9363060..bac7728 100644
--- a/Excalidraw/BellmanEquation.excalidraw.md
+++ b/Excalidraw/BellmanEquation.excalidraw.md
@@ -281,10 +281,10 @@ g4bt3gEC1L91AFY3Cd3I64379vjaf39V0o5p3bVz/aqOGqvLTk3tTyTYMPlNkA8522jiTg9W+dqA56Pk
 
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 ```
 %%
\ No newline at end of file
diff --git a/Running Start/CSB320 - Machine Learning Concepts/Class 6-4 (Reinforcement Learning).md b/Running Start/CSB320 - Machine Learning Concepts/Class 6-4 (Reinforcement Learning).md
index 3e289fe..4990a02 100644
--- a/Running Start/CSB320 - Machine Learning Concepts/Class 6-4 (Reinforcement Learning).md	
+++ b/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 
\ No newline at end of file
+		- this is what backpropagates future rewards 
+	- during exploration phase paths are usually explored randomly to attempt to find the q values for each path
+	- 
\ No newline at end of file