Artificial Intelligence, Finance, Reinforcement Learning, Data Science, Innovation.
update_policy(self, state, action, reward, next_state, done): old_value = self.q_table[state, action] next_max = np.max(self.q_table[next_state]) new_value = (1 - self.learning_rate) * old_value + self.learning_rate * (reward + self.discount_factor * next_max * (not done)) self.q_table[state,