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

@@ -1,4 +1,15 @@
 #rs/class/csb320 #rs/notes 
 - - -
 - Models are trained by learning from their mistakes
-- 
+- multi-armed bandits
+	- choose action from k possibilities
+		- receive a reward
+	- reward is dependent on the action taken
+	- explore vs. exploit
+		- exploit is taking the greedy action, the on that is known to produce the greatest reward
+		- explore is taking other random actions to learn values
+		- a combination of the two produce the best results
+	- $\varepsilon$-greedy strategy
+		- with probability $\varepsilon$ take a random action, with probability $1-\varepsilon$ take the best known actions
+		- $\varepsilon$ often starts high and decreases over time
+	-