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DQN PyTorch Beginners Tutorial #4 – Implement Epsilon-Greedy & Debug the Training Loop
Welcome to the fourth tutorial in our DQN PyTorch series! In this tutorial, we will be implementing the epsilon-greedy policy for our DQN agent and debugging the training loop to ensure smooth training.
Implementing Epsilon-Greedy Policy
The epsilon-greedy policy is a common technique used in reinforcement learning to balance exploration and exploitation. It works by choosing a random action with probability epsilon and the best action according to the current Q-values with probability 1-epsilon.
To implement the epsilon-greedy policy in our DQN agent, we need to modify the action selection logic in our agent’s `select_action` method. We will generate a random number between 0 and 1, and if this number is less than epsilon, we will choose a random action. Otherwise, we will choose the action with the highest Q-value.
Debugging the Training Loop
During training, it’s important to monitor the training loss, rewards, and other metrics to ensure that the agent is learning effectively. In this tutorial, we will add debug statements to our training loop to print out these metrics and track the progress of our agent.
We will also visualize the training progress using matplotlib graphs to observe how the training loss decreases and rewards increase over time.
Conclusion
In this tutorial, we implemented the epsilon-greedy policy in our DQN agent and debugged the training loop to monitor the training progress. By carefully tuning the epsilon value and monitoring the training metrics, we can improve the effectiveness of our DQN agent and ensure smooth training.
Stay tuned for the next tutorial in our DQN PyTorch series, where we will dive deeper into advanced techniques for training and optimizing our DQN agent!
Waiting for the next video
could you do balancing double inverse pendulum example?