请补全上述代码

非常抱歉，我发现我的回答中有些地方没有完全补全代码。以下是完整的代码：

import gym 
import math 
import random 
import torch 
import torch.nn as nn 
import torch.nn.functional as F 
import torch.optim as optim  

from Q_net import Net  

class Agent(object):
    def __init__(self, **kwargs):
        for key, value in kwargs.items():
            setattr(self, key, value)

        self.eval_net = Net(self.state_space_dim, self.action_space_dim)
        self.optimizer = optim.Adam(self.eval_net.parameters(), lr=self.lr)
        self.buffer = []
        self.steps = 0 

    def act(self, s0):
        self.steps += 1
        epsi = self.epsi_low + (self.epsi_high - self.epsi_low) * (math.exp(-1.0 * self.steps / self.decay))
        if random.random() < epsi:
            a0 = random.randrange(self.action_space_dim)
        else:
            s0 = torch.tensor(s0, dtype=torch.float).view(1, -1)
            # 根据状态选择最佳动作
            with torch.no_grad():
                q_values = self.eval_net(s0)
                a0 = q_values.argmax(dim=1)

        return a0 

    def put(self, *transition):
        if len(self.buffer) == self.capacity:
            self.buffer.pop(0)
        self.buffer.append(transition)

    def learn(self):
        if (len(self.buffer)) < self.batch_size:
            return 

        samples = random.sample(self.buffer, self.batch_size)
        s0, a0, r1, s1 = zip(*samples)
        s0 = torch.tensor(s0, dtype=torch.float)
        a0 = torch.tensor(a0, dtype=torch.long).view(self.batch_size, -1)
        r1 = torch.tensor(r1, dtype=torch.float).view(self.batch_size, -1)
        s1 = torch.tensor(s1, dtype=torch.float)

        loss_fn = nn.MSELoss()

        # 定义损失函数
        q_next = self.eval_net(s1).max(dim=1, keepdim=True)[0].detach()
        q_target = r1 + self.gamma * q_next
        q_eval = self.eval_net(s0).gather(1, a0)
        loss = loss_fn(q_eval, q_target)

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

希望这次的回答能够帮助到您，如有疑问请随时提问。