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self.current_state = None self.current_action = None def choose_action(self, state): # Exploration-exploitation trade-off if np.random.uniform(0, 1) < self.exploration_prob: return np.random.choice(self.num_actions) # Explore else: feature_index = np.argmax(state) return np.argmax(self.q_table[:,