def replay(self):
    if len(self.recent_memory) < minibatch_size:
        return

    minibatch = random.sample(self.recent_memory, minibatch_size)

    state_batch, q_values_batch = [], []

    for state, action, reward, next_state, done in minibatch:
        # Get predictions for all actions for the current state.
        q_values = self.model.predict(state)

        # If we're not done, add on the future predicted reward at the discounted rate.
        if done:
            q_values[0][action] = reward
        else:
            future_reward = np.amax(self.model.predict(next_state)[0])
            q_values[0][action] = reward + self.gamma * future_reward

        state_batch.append(state[0])
        q_values_batch.append(q_values[0])

    # Re-fit the model to move it closer to this newly calculated reward.
    self.model.fit(np.array(state_batch), np.array(q_values_batch))

def replay(self):
    if len(self.recent_memory) < minibatch_size:
        return

    minibatch = random.sample(self.recent_memory, minibatch_size)

    for state, action, reward, next_state, done in minibatch:
        # Get predictions for all actions for the current state.
        q_values = self.model.predict(state)

        # If we're not done, add on the future predicted reward at the discounted rate.
        if done:
            q_values[0][action] = reward
        else:
            future_reward = np.amax(self.model.predict(next_state)[0])
            q_values[0][action] = reward + self.gamma * future_reward

        self.model.fit(state, q_values, epochs=1, verbose=0)