Update environment.py

environment.py

@@ -1,70 +1,65 @@
-import asyncio
-import math
-import random
-from models import State
-
-class CloudCostEnv:
-    def __init__(self, task="medium"):
-        self.task = task
-        self.reset_vars()
-
-    def reset_vars(self):
-        self.steps = 0
-        self.servers = 5
-        self.is_spot = False
-
-    async def reset(self):
-        self.reset_vars()
-        return self.get_current_state()
-
-    def get_current_state(self):
-        # This simulates traffic based on the "Task" (Easy/Medium/Hard)
-        if self.task == "easy":
-            load = 450
-        else:
-            # Sine wave simulates day/night traffic peaks
-            load = int(500 + 350 * math.sin(self.steps / 5))
-            
-        # Physics: More servers = lower latency. Load > Servers = high latency.
-        capacity = self.servers * 100
-        latency = max(20, (load / (capacity + 1)) * 150)
-        
-        # Cost: Spot is 75% cheaper than On-Demand
-        base_rate = 0.60
-        current_rate = base_rate * 0.25 if self.is_spot else base_rate
-        cost = self.servers * current_rate
-        
-        return State(
-            cpu_usage=min(100.0, (load / (capacity + 1)) * 100),
-            active_servers=self.servers,
-            hourly_burn_rate=round(cost, 2),
-            response_latency=round(latency, 2),
-            incoming_load=load
-        )
-
-    async def step(self, action_int):
-        # Apply the action from the AI
-        if action_int == 1: 
-            self.servers += 1
-        elif action_int == 2: 
-            self.servers = max(1, self.servers - 1)
-        elif action_int == 3: 
-            self.is_spot = not self.is_spot
-        
-        self.steps += 1
-        state = self.get_current_state()
-        
-        # Calculate Reward (The Score)
-        reward = 1.0
-        if state.response_latency > 150: reward -= 0.7  # Penalty for being slow
-        if state.hourly_burn_rate > 5.0: reward -= 0.5   # Penalty for overspending
-        
-        # Episode ends after 20 steps for the grader
-        done = self.steps >= 20
-        return state, reward, done
-
-    async def state(self):
-        return self.get_current_state()
-
-    async def close(self):
+import asyncio
+import math
+import random
+from models import State
+
+class CloudCostEnv:
+    def __init__(self, task="medium"):
+        self.task = task
+        self.reset_vars()
+
+    def reset_vars(self):
+        self.steps = 0
+        self.servers = 5
+        self.is_spot = False
+
+    def reset(self):
+        self.reset_vars()
+        return self.get_current_state()
+
+    def get_current_state(self):
+        if self.task == "easy":
+            load = 450
+        elif self.task == "hard":
+            load = int(500 + 350 * math.sin(self.steps / 2)) + random.randint(-100, 100)
+        else:  # medium
+            load = int(500 + 350 * math.sin(self.steps / 5))
+
+        capacity = self.servers * 100
+        latency = max(20, (load / (capacity + 1)) * 150)
+
+        base_rate = 0.60
+        current_rate = base_rate * 0.25 if self.is_spot else base_rate
+        cost = self.servers * current_rate
+
+        return State(
+            cpu_usage=min(100.0, (load / (capacity + 1)) * 100),
+            active_servers=self.servers,
+            hourly_burn_rate=round(cost, 2),
+            response_latency=round(latency, 2),
+            incoming_load=load
+        )
+
+    async def step(self, action_int):
+        if action_int == 1:
+            self.servers += 1
+        elif action_int == 2:
+            self.servers = max(1, self.servers - 1)
+        elif action_int == 3:
+            self.is_spot = not self.is_spot
+
+        self.steps += 1
+        state = self.get_current_state()
+
+        reward = 1.0
+        if state.response_latency > 150: reward -= 0.7
+        if state.hourly_burn_rate > 5.0: reward -= 0.5
+
+        done = self.steps >= 20
+        return state, reward, done
+
+    async def state(self):
+        return self.get_current_state()
+
+    async def close(self):
         pass