Method learn

1-grid-world/4-q_learning.py:17–21

(self, state, action, reward, next_state)

    # update q function with sample <s, a, r, s'>
    def learn(self, state, action, reward, next_state):
        current_q = self.q_table[state][action]
        # using Bellman Optimality Equation to update q function
        new_q = reward + self.discount_factor * max(self.q_table[next_state])
        self.q_table[state][action] += self.learning_rate * (new_q - current_q)

    # get action for the state according to the q function table
    # agent pick action of epsilon-greedy policy