Deep Reinforcement Learning With Python | Part 3| Using Tensorboard to Analyse Trained Models
In The Previous Parts:
- First part explained and created the game environment.
- Second part discussed the process of training the DQN, explained DQNs and gave reasons to choose DQN over Q-Learning.
In This Part:
We are going to:
- Use Tensorboard to visualize the goodness of trained models.
- Explain the way of loading and trying a trained model.
- Use the best model and let it play the game.
Using Tensorboard:
1- Use a modified Tensorboard to make Tensorboard logs the data from all episodes in the training process in one log file instead of making a new log file every time we fit the model.
The next code of the ModifiedTensorBoard is from this blog by sentdex, I just changed it a little bit to make it run on TensorFlow2.0:
2- Define an object of the modified tensorboard in the __init__ of the agent class:
PATH and name are used to define the full path where the log file will be saved in.
3- Pass the modified tensorboard as a callback when fitting the model:
4- At the start of each episode :
5- To update the logs, use the next line:
Visualising the logs :
Open the terminal on the directory where the “logs” folder is and run:
tensorboard --logdir="logs/"
A browser window will appear:
You can try the other options by yourself.
By using these visualisations we can see the relations between logged variables, such as epsilon and max_reward.
Loading and Using Trained Models:
Some Shots of Agents Playing The Game:
Other parts of this series:
