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def retrain(self, batch_size):
minibatch = random.sample(self.expirience_replay, batch_size)
for state, action, reward, next_state, terminated in minibatch:
target = self.q_network.predict(state)
if terminated:
target[0][action] = reward
else:
t = self.target_network.predict(next_state)
target[0][action] = reward + self.gamma * np.amax(t)
self.q_network.fit(state, target, epochs=1, verbose=0)
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