Deep Q-Network applied to Classical Control
==============================

Here you will find a DQN model trained to solve the classical control "CartPole-v0" problem as part of "Coding Challenge for Fatima Fellowship". The agent was trained for 1000 episodes and the framework used to trained the model is Pytorch.

* Additionally to the model, there are three files corresponding to the weights of the neural network after 100, 500 and 1000 episodes.

In order to load the weights in the model, you may use something like this:

```
model = TheModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
model.eval()
```


{}
region:us
TAGS
#region-us 

Deep Q-Network applied to Classical Control
==============================

Here you will find a DQN model trained to solve the classical control "CartPole-v0" problem as part of "Coding Challenge for Fatima Fellowship". The agent was trained for 1000 episodes and the framework used to trained the model is Pytorch.

* Additionally to the model, there are three files corresponding to the weights of the neural network after 100, 500 and 1000 episodes.

In order to load the weights in the model, you may use something like this:




TAGS
#region-us 

passage: TAGS
#region-us 
