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Excalidraw/BellmanEquation.excalidraw.md
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```
%%
Running Start/CSB320 - Machine Learning Concepts/Class 6-4 (Reinforcement Learning).md
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@@ -37,3 +37,5 @@
- 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
- during exploration phase paths are usually explored randomly to attempt to find the q values for each path
-