dqn.py 文件源码

def build_functions(self):
        S = Input(shape=self.state_size)
        NS = Input(shape=self.state_size)
        A = Input(shape=(1,), dtype='int32')
        R = Input(shape=(1,), dtype='float32')
        T = Input(shape=(1,), dtype='int32')
        self.build_model()
        self.value_fn = K.function([S], self.model(S))

        VS = self.model(S)
        VNS = disconnected_grad(self.model(NS))
        future_value = (1-T) * VNS.max(axis=1, keepdims=True)
        discounted_future_value = self.discount * future_value
        target = R + discounted_future_value
        cost = ((VS[:, A] - target)**2).mean()
        opt = RMSprop(0.0001)
        params = self.model.trainable_weights
        updates = opt.get_updates(params, [], cost)
        self.train_fn = K.function([S, NS, A, R, T], cost, updates=updates)