vault backup: 2024-10-25 09:24:34

.obsidian/workspace.json

@@ -34,9 +34,23 @@
               "icon": "lucide-file",
               "title": "7 RL"
             }
+          },
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+            "type": "leaf",
+            "state": {
+              "type": "markdown",
+              "state": {
+                "file": "conflict-files-obsidian-git.md",
+                "mode": "source",
+                "source": false
+              },
+              "icon": "lucide-file",
+              "title": "conflict-files-obsidian-git"
+            }
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+        "currentTab": 2
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@@ -192,24 +206,24 @@
       "obsidian-git:Open Git source control": false
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-    "Pasted image 20241023135924.png",
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+    "Autonomous Networking/notes/7 RL.md",
+    "conflict-files-obsidian-git.md",
+    "Biometric Systems/images/Pasted image 20241024100002.png",
+    "Biometric Systems/images/Pasted image 20241024095704.png",
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+    "Biometric Systems/images/Pasted image 20241024091433.png",
+    "Biometric Systems/images/Pasted image 20241024091235.png",
     "q&a.md",
     "Autonomous Networking/notes/3 WSN MAC.md",
     "8.md",
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@@ -222,7 +236,6 @@
     "Autonomous Networking/notes/4 WSN Routing.md",
     "Autonomous Networking/notes/2 RFID.md",
     "Foundation of data science/notes/1 CV Basics.md",
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Autonomous Networking/notes/7 RL.md

@@ -84,13 +84,13 @@ exploration vs exploitation dilemma:
 - emits observation Ot
 - emits scalar reward Rt
 
-
-
 **agent state:** the view of the agent on the environment state, is a function of history
-- the function of the history is involved in taking the next decision
-- the state representation defines what happens next
-- ...
-
+- the history is involved in taking the next decision:
+	- agent selects actions
+	- environment selects observations/rewards
+- the state information is used to determine what happens next
+	- state is a function of history: $S_t = f(H_t)$
+	
 #### Inside the agent
 one or more of these components
 - **Policy:** agent's behavior function
@@ -107,9 +107,10 @@ one or more of these components
 	- is a prediction of future reward
 	- used to evaluate the goodness/badness of states
 	- values are prediction of rewards
-	- Vp(s) = Ep[yRt+1 + y^2Rt+2 ... | St = s]
+	- $V_\pi(s) = Ep[yRt+1 + y^2Rt+2 ... | St = s]$
 - **Model:**
 	- predicts what the environment will do next
+	- may predict the resultant next state and/or the next reward
 	- many problems are model free
 
 back to the original problem: