I am solving combinational optimization problem with DQN.
My goal is to reach the optimal state
I just revised a little from the following pytorch DQN tutorials
https://tutorials.pytorch.kr/intermediate/reinforcement_q_learning.html 1

class DQN(nn.Module):

    def __init__(self, h, w, outputs):
        super(DQN, self).__init__()
        self.channel = 7
        self.conv1 = nn.Conv2d(self.channel, 16, kernel_size=3, stride=2)
        self.bn1 = nn.BatchNorm2d(16)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=2)
        self.bn2 = nn.BatchNorm2d(32)
        self.conv3 = nn.Conv2d(32, 32, kernel_size=1, stride=2)
        self.bn3 = nn.BatchNorm2d(32)


        def conv2d_size_out(size, kernel_size=5, stride=2):
            return (size - (kernel_size - 1) - 1) // stride + 1

        convw = 2
        convh = 2
        linear_input_size = convw * convh * 32
        self.head = nn.Linear(linear_input_size, outputs)


    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.relu(self.bn3(self.conv3(x)))
        return self.head(x.view(x.size(0), -1))


def select_action(state):
    global steps_done
    sample = random.random()
    #sample = 1.0
    eps_threshold = EPS_END + (EPS_START - EPS_END) * \
        math.exp(-1. * steps_done / EPS_DECAY)
    steps_done += 1
    if sample > eps_threshold:
        with torch.no_grad():
            # t.max (1)은 각 행의 가장 큰 열 값을 반환합니다.
            # 최대 결과의 두번째 열은 최대 요소의 주소값이므로,
            # 기대 보상이 더 큰 행동을 선택할 수 있습니다.
            return policy_net(state).max(1)[1].view(1, 1)
    else:
        return torch.tensor([[random.randrange(n_actions)]], device=device, dtype=torch.long)


def optimize_model():
    if len(memory) < BATCH_SIZE:
        return
    transitions = memory.sample(BATCH_SIZE)

    batch = Transition(*zip(*transitions))

    non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,
                                          batch.next_state)), device=device, dtype=torch.bool)

    non_final_next_states = torch.cat([s for s in batch.next_state
                                                if s is not None])
    state_batch = torch.cat(batch.state)
    action_batch = torch.cat(batch.action)
    reward_batch = torch.cat(batch.reward)


    state_action_values = policy_net(state_batch).gather(1, action_batch)  # double DQN
    
    next_state_values = torch.zeros(BATCH_SIZE, device=device, dtype=torch.double)
    next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach()



    expected_state_action_values = (next_state_values * GAMMA) + reward_batch



    loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1))

    optimizer.zero_grad()
    loss.backward()
    for param in policy_net.parameters():
        param.grad.data.clamp_(-1, 1)
    optimizer.step()


After some learning, all output gives same value regardless of input
So, I changed the reward to 0 for every action, but the situation was same
Please give me an advice