Reinforcement Learning, Ciencia De Dados, Data Science, Machine Learning.
np.random.random() < self.epsilon: action = np.random.choice(self.actions) else: state_idx = self.smap[self.state] action = argmax(self.q_values[state_idx,:]) return action def start(self,response): """ called after env starts """ self.state = response[0] action = self.policy() self.prev_state =