Artificial Intelligence, Finance, Reinforcement Learning, Data Science, Innovation.
np.zeros((state_dim, n_actions)) def choose_action(self, state): if np.random.rand() < self.exploration_rate: action = np.random.randint(self.n_actions) else: action = np.argmax(self.q_table[state]) print(f"Choosing action: {action} for state: {state}") # Debug statement return action def