policy = ts.policy.DQNPolicy(net, optim, discount_factor=0.9, estimation_step=estimation_step, target_update_freq=320)的各个参数是什么意思
时间: 2024-03-04 21:48:23 浏览: 109
这是一个深度强化学习中的DQN算法的策略函数,其中各个参数的含义如下:
- `net`: 神经网络模型,用于近似值函数;
- `optim`: 优化器,用于更新神经网络模型的参数;
- `discount_factor`: 折扣因子,用于衡量未来奖励的价值,通常取值为0.9到0.99之间;
- `estimation_step`: 估计步数,用于计算多步回报,通常取值为1到10之间;
- `target_update_freq`: 目标网络更新频率,即每隔多少步更新一次目标网络,通常取值为100到1000之间。
这些参数的设置会影响DQN算法的性能和收敛速度。
相关问题
ts.policy.DQNPolicy(net, optim, discount_factor=0.9, estimation_step=estimation_step, target_update_freq=320)的DQN执行过程是怎样的
`ts.policy.DQNPolicy`是Tianshou库中的一个类,它的作用是实现DQN算法的执行过程。具体来说,`DQNPolicy`的执行过程如下:
1. 获取当前状态(state);
2. 使用当前策略网络(policy_net)对状态进行前向传播,得到当前状态下每个动作(action)的Q值;
3. 根据一定的策略(如贪心策略)选择当前状态下的动作;
4. 执行该动作,观察环境反馈(包括奖励和下一个状态);
5. 将当前状态、动作、奖励和下一个状态存储到经验回放缓冲区中;
6. 从经验回放缓冲区中随机取出一批经验,计算当前状态下每个动作的Q值(即评估网络值)和目标状态下每个动作的Q值(即目标网络值);
7. 计算损失函数并更新策略网络的参数;
8. 如果当前步数是目标网络更新步数的倍数,则使用当前策略网络更新目标网络。
其中,参数`net`是策略网络,参数`optim`是优化器,`discount_factor`是折扣因子,`estimation_step`是n-step TD估计中的n,`target_update_freq`是目标网络更新的频率。
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau
The `scheduler` variable is an instance of the `ReduceLROnPlateau` class from the PyTorch `optim.lr_scheduler` module. This class implements a learning rate scheduler that monitors a specified metric and reduces the learning rate if the metric does not improve for a certain number of epochs.
The `ReduceLROnPlateau` scheduler takes the following parameters:
- `optimizer`: The optimizer that is being used to train the model.
- `mode`: Specifies whether the metric being monitored should be minimized or maximized. Possible values are `'min'`, `'max'`, or `'auto'` (which infers the mode based on the metric name).
- `factor`: The factor by which the learning rate is reduced. For example, if `factor=0.1`, the learning rate will be reduced by a factor of 0.1 (i.e., the new learning rate will be 0.1 times the old learning rate).
- `patience`: The number of epochs to wait before reducing the learning rate if the metric does not improve.
- `verbose`: Specifies whether to print information about the learning rate changes.
- `threshold`: The threshold for measuring the new optimum, to only focus on significant changes.
- `threshold_mode`: Specifies whether the threshold is relative (`'rel'`) or absolute (`'abs'`).
The `scheduler.step()` method is called at the end of each epoch to update the learning rate based on the monitored metric.