I've Been Doing This Wrong The Whole Time ... The Right Way to Save Models In PyTorch

TL;DR

Learn how to save models for deep reinforcement learning agents in PyTorch, including the state of the agent's weights, biases, optimizer, and other parameters, allowing for easy resuming of training.

Transcript

now it turns out all of us make mistakes and one of my big mistakes is how I have been saving models for deep reinforcement learning agents it turns out there is a much better way that allows you to save not only the state of the agent's weights and biases but also the state of the optimizer in the pytorch framework at any particular moment and you... Read More

Key Insights

• 🏋️ Saving models in deep reinforcement learning agents requires saving the state of not only the weights and biases but also the optimizer and other important parameters.
• 💾 PyTorch provides functionalities like 'def save models' and 'def load models' to handle the process of saving and loading models.
• 👻 The improved model saving approach allows for easy resuming of training from any particular moment, even including the Epsilon value for the agent.
• 🤪 This approach opens up possibilities for transfer learning between different environments, such as going from the cart pole to the lunar lander, and enables partial training as well.
• 😒 The use of this approach can enhance the concept of intrinsic curiosity and potentially improve performance in deep reinforcement learning agents.
• 🥳 Further improvements and discussions on these topics are expected to be made in the upcoming days.
• 💬 Leaving comments and subscribing can help in surfacing the content and receiving more helpful tutorials.

Questions & Answers

Q: What were the mistakes made in saving models for deep reinforcement learning agents?

The mistakes included not saving the state of the optimizer and not saving important parameters like Epsilon for the agent. These mistakes have been rectified in the new approach.

Q: How can you save the state of the agent's weights and biases using PyTorch?

You can save the state of the agent's weights and biases by saving them in a dictionary, using keys like 'Epsilon', 'qeval', and 'Qnext', and then saving the dictionary to a checkpoint file.

Q: What is the purpose of the 'load models' function?

The 'load models' function is used to load the saved models and their respective states, including the state of the optimizer and the policy Epsilon, so that training can be resumed from where it left off.

Q: Can this approach be used for partial training or transfer learning?

Yes, this approach can also be used for saving partial training and for transfer learning, especially when dealing with environments like the Atari Library, where different vector input spaces may exist.

Summary & Key Takeaways

• The content discusses a better way to save models for deep reinforcement learning agents in PyTorch, which includes saving the state of the agent's weights, biases, optimizer, and other important parameters.

• The code in the video is modified to implement this improved functionality for model saving.

• Functions for saving and loading models are added to handle the process of saving and resuming training from any particular moment.