:children_crossing: How to save and load policy network for testing.

After training the agent, many people are not sure how to save and load the policy network after training and see how the agent actually performs in a simulation environment.
很多人在完成agent 的训练之后，不清楚要如何保存并加载训练完成后的 policy network，并在仿真环境中看看这个agent的实际表现。

Here is the code to (take `Pendulum` env for example):
- train the agent and save the policy network
- load the policy network and use it to map the state to get the action.

下面是两个代码例子（举`Pendulum` 环境为例）:
- 训练agent并保存policy network
- 加载policy network 并使用它 对 state 映射得到 action

### train the agent and save the policy network
训练agent并保存policy network

https://github.com/AI4Finance-Foundation/ElegantRL/blob/68bf0ea4ef3fb461026ece8897deabb92aeead32/examples/demo_A2C_PPO.py#L14-L18

https://github.com/AI4Finance-Foundation/ElegantRL/blob/68bf0ea4ef3fb461026ece8897deabb92aeead32/elegantrl/train/run.py#L99

The process will keep saving policy network (actor) in `cwd="./Pendulum_PPO_0/act.pt"` (current working directory) during training.
程序会在训练中，持续保存 saving policy network (actor) 在当前的工作目录下 `cwd="./Pendulum_PPO_0/act.pt"` (current working directory)

https://github.com/AI4Finance-Foundation/ElegantRL/blob/68bf0ea4ef3fb461026ece8897deabb92aeead32/elegantrl/train/run.py#L92

### load the policy network and use it to map the state to get the action.
加载policy network 并使用它 对 state 映射得到 action

https://github.com/AI4Finance-Foundation/ElegantRL/blob/68bf0ea4ef3fb461026ece8897deabb92aeead32/examples/demo_A2C_PPO.py#L662

The following code load the policy netowrk (actor) from disk:
下面的代码从硬盘里 加载了 policy netowrk (actor)：

https://github.com/AI4Finance-Foundation/ElegantRL/blob/68bf0ea4ef3fb461026ece8897deabb92aeead32/examples/demo_A2C_PPO.py#L679-L682

The following code map state to action using policy netowrk (actor):
下面的代码使用 policy netowrk (actor) 将 state 映射到 action：

https://github.com/AI4Finance-Foundation/ElegantRL/blob/68bf0ea4ef3fb461026ece8897deabb92aeead32/examples/demo_A2C_PPO.py#L699-L705