Update learning_loop.py

human_machine/learning_loop.py

@@ -1 +1,59 @@
-print("Hello World")
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Callable, List
+
+@dataclass
+class LearningCycle:
+    """
+    Represents a single learning cycle iteration.
+
+    Attributes
+    ----------
+    iteration : int
+        The iteration number (starting from 1).
+    state : Any
+        The state after the update function is applied.
+    reward : float
+        The reward computed for this cycle.
+    """
+    iteration: int
+    state: Any
+    reward: float
+
+class LearningLoop:
+    """
+    Executes an iterative learning loop with update and reward functions.
+    """
+    def __init__(self, update_fn: Callable[[Any], Any], reward_fn: Callable[[Any], float], max_iter: int = 5) -> None:
+        self.update_fn = update_fn
+        self.reward_fn = reward_fn
+        self.max_iter = max_iter
+
+    def run(self, initial_state: Any) -> List[LearningCycle]:
+        """
+        Run the learning loop over a number of iterations.
+
+        Parameters
+        ----------
+        initial_state : Any
+            The starting state for the learning process.
+
+        Returns
+        -------
+        List[LearningCycle]
+            A list of learning cycles capturing state and reward at each step.
+        """
+        cycles: List[LearningCycle] = []
+        state = initial_state
+        for i in range(1, self.max_iter + 1):
+            state = self.update_fn(state)
+            reward = self.reward_fn(state)
+            cycles.append(LearningCycle(i, state, reward))
+        return cycles
+
+if __name__ == "__main__":
+    # Example usage: increment state and reward as negative distance from target 10
+    loop = LearningLoop(lambda x: x + 1, lambda x: -abs(10 - x), max_iter=3)
+    for cycle in loop.run(0):
+        print(cycle)