Create quantum_inspired_reinforcement_learning.py

evolution/machine_learning/quantum_inspired_reinforcement_learning.py

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+# quantum_inspired_reinforcement_learning.py
+import numpy as np
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Dense
+
+class QuantumInspiredReinforcementLearning:
+    def __init__(self, num_actions, num_states):
+        self.num_actions = num_actions
+        self.num_states = num_states
+
+    def _evaluate_reward(self, state, action):
+        # Implement reward function
+        pass
+
+    def _train_model(self, X, y):
+        model = Model(inputs=Input(shape=(X.shape[1],)), outputs=Dense(y.shape[1], activation='softmax')(Dense(64, activation='relu')(Input(shape=(X.shape[1],)))))
+        model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+        model.fit(X, y, epochs=10, batch_size=32, validation_data=(X, y))
+        return model
+
+    def train(self, X, y):
+        model = self._train_model(X, y)
+        return model
+
+# Example usage:
+X = np.random.rand(100, 10)
+y = np.random.rand(100, 10)
+qirl = QuantumInspiredReinforcementLearning(num_actions=4, num_states=10)
+model = qirl.train(X, y)