Curriculum Learning for LSTMs: Smarter Predictions with Code Explanation

Training an Autoregressive LSTM Model for Vessel Simulation

In the world of AI-driven sequence modeling, LSTM networks have proven invaluable. In our latest project, I implemented an Autoregressive LSTM Model to predict vessel states in a simulated environment. But how does this model transition from supervised training to self-generated predictions? Let’s break it down.

1. Why Use an Autoregressive LSTM?

LSTM models are great at capturing long-term dependencies, but for stable long-horizon predictions, they need to gradually shift from relying on ground-truth inputs (Non-Autoregressive) to self-prediction (Autoregressive). We achieve this through curriculum learning, where the model is eased into AR mode over time.

2. Training Breakdown

• Model Architecture: Two stacked LSTM layers (128 units) with dropout to prevent overfitting.
• NAR vs. AR Steps: Initially, we use ground-truth inputs (NAR), but gradually increase AR reliance.
• Loss Function: A weighted sum of NAR and AR losses ensures smooth transition.
• Hyperparameters: Batch size (512), learning rate (0.005 with Adam optimizer), and max AR steps (5) were fine-tuned for stability.

3. Why This Approach Works

By increasing AR steps progressively, our model learns long-term dependencies without abrupt shifts. Using ReduceLROnPlateau, I dynamically adjust the learning rate to maintain stability. Our evaluation ensures that the best-performing model (lowest test loss) is saved for future retraining.

4. What’s Next?

I have integrated this trained model into a custom Gym environment to assess its real-world applicability. Want to see how it performs? Watch the full video to see the results! 🎥

Here is the code: