Casascius Coins: Introduction and Rules

Introduction

The message "Agent stopped due to max iterations" is a brief system notification commonly encountered in various iterative processes (model training, numerical optimizers, RL agents, simulations, etc.). It merely reports a fact—the iteration limit has been reached—and does not provide a full diagnosis. Below is a clear explanation of the causes, a step-by-step diagnostic guide, practical recommendations, and real-world examples of configuring max_iterations across different scenarios. This material is designed for practitioners with basic knowledge of machine learning and algorithms (beginners will find the "What to do first" section helpful, while experienced users should look at the examples and reference sections).

What the Message Means

• Literally: The process was forcibly terminated because a predefined iteration limit was reached (parameter max_iterations / max_iter / max_steps, etc.).
• Not always an error: This can be an expected safety constraint or a resource limit.
• May signal a problem: It indicates the algorithm failed to converge or complete correctly before the limit was hit.

Typical Areas Where It Occurs

• Neural Network and Classical Model Training: (epochs/iterations/gradient steps).
• Numerical Methods and Optimizers: (Newton, BFGS, gradient descent).
• Reinforcement Learning: (steps per episode, number of episodes, or timesteps).
• Search and Simulation Algorithms: (step limits to prevent wandering).
• Scripts/Pipelines: "Safety" limits used to prevent infinite loops.

Possible Causes (Briefly)

• The limit is set too low or, conversely, is inadequately high.
• Poor hyperparameter tuning → lack of convergence.
• The task is too complex for the current architecture/policy.
• Missing or incorrectly implemented early stopping criteria.
• A bug in the loop exit logic—termination conditions are never met.

How to Diagnose (Practical Checklist)

1. Logs and Metrics

   • Check how many iterations were completed and how metrics (loss / reward / grad_norm) changed over time.
   • Log values every K iterations (e.g., every 10 or 100).

2. Rapid Reproduction

   • Run on a reduced dataset or in debug mode to speed up the cycle and catch issues.

3. Benchmarking

   • Run a previously working configuration (if available) and compare the metric dynamics.

4. Checkpoints

   • Save the model/state every N iterations so you can resume or analyze the progress.

5. Code Audit

   • Ensure the convergence criteria check is being performed and actually triggers an exit.
   • Add unit tests for loop exit conditions.

Practical Recommendations (with Specifics)

1. Selecting an Adequate max_iterations

   • Quick experiments: Set a low max_iterations (e.g., 10–50) to quickly verify logic.
   • Small tasks/prototypes: max_epochs 10–50.
   • Standard models: Start with 100–500 epochs and increase as needed.
   • Heavy tasks/large networks: 1000+ epochs only with checkpoints and monitoring in place.

2. Early Stopping and Patience

   • Implement early stopping based on a validation metric with a patience parameter (e.g., patience=10 or 20).
   • For Keras: Use Callback EarlyStopping
     https://keras.io/api/callbacks/early_stopping/

3. Technology-Specific Configuration Examples

   • scikit-learn LogisticRegression: If a ConvergenceWarning appears, increase max_iter from 100 to 1000. Documentation:
     https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
   • SciPy optimize (scipy.optimize.minimize): The maxiter option is usually set to 50–500 depending on complexity:
     https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
   • PyTorch/TF Training: Use checkpoint saving, log loss every N batches, and implement early stopping; initial epoch values 10–100+ depending on data.
   • RL (OpenAI Gym / Stable Baselines): Episode step limits, e.g., max_steps_per_episode=200 for CartPole; for complex environments—thousands of steps. See environment docs:
     https://www.gymlibrary.dev/

4. Debugging Tricks

   • Run with an artificially reduced max_iterations to observe behavior and errors faster.
   • Log gradients/gradient norms to understand if the training process is "stuck."
   • Add a time-based timeout in addition to the iteration count.

5. Recovery and Checkpoints

   • Save intermediate states (every N iterations) to allow resuming training from the last checkpoint.

6. When It Is a Bug

   • Write unit tests for the loop: scenarios where the exit condition is true/false.
   • Look for infinite loops: the exit condition depends on a variable that is not updated inside the loop.

Real-World Examples from Practice

1. scikit-learn: Logistic Regression

   • Problem: When training on large feature matrices, the model throws a ConvergenceWarning and "Agent stopped due to max iterations."
   • Action: Increase max_iter=1000, enable feature scaling via StandardScaler, decrease tol, check regularization (C).
   • Result: Convergence reached, though iterations increased—added validation-based early stopping and checkpoints (logs now show "Stopped: max_iter=1000 reached").

2. SciPy Optimization

   • Problem: The minimizer stopped upon reaching maxiter=100 without reaching the desired precision.
   • Action: Increase maxiter to 500, decrease tolerance, try a different method (BFGS → L-BFGS-B), normalize inputs.
   • Result: Algorithm converged; grad_norm and function values recorded at each restart.

3. RL (CartPole → Complex Environment)

   • Problem: The agent regularly stopped at max_steps_per_episode=200 without improving average reward.
   • Action: Increase the limit to 500 for evaluation, decrease learning rate, add an entropy bonus to boost exploration, simplify the environment during debugging.
   • Result: After tuning exploration/exploitation, the agent improved average reward and achieved a more stable policy.

4. Loop Logic Bug

   • Problem: A while loop exit condition checked a variable updated only in a test block; it remained unchanged in production → stopped by max_iterations.
   • Action: Refactored code, added unit tests for completion scenarios, logged state changes.
   • Result: Bug fixed, correct termination based on convergence criteria now works.

Short Recommendations for Different Levels

• For Beginners:

  • First, check logs and run on a small dataset.
  • Use standardized implementations (scikit-learn/Keras) and their built-in options (max_iter, EarlyStopping).
  • Read errors and warnings (ConvergenceWarning).

• For Experienced Users:

  • Implement monitoring (loss, grad_norm, GPU/CPU utilization) and automated rules (alerts on stagnation).
  • Try different optimizers/methods and profile bottlenecks.
  • Write tests for critical parts of the iterative logic.

Useful Resources and Documentation

• scikit-learn LogisticRegression:
  https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

• SciPy optimize (minimize):
  https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html

• Keras EarlyStopping:
  https://keras.io/api/callbacks/early_stopping/

• OpenAI Gym / Environments and Step Limits:
  https://www.gymlibrary.dev/

• General Monitoring and MLOps Practices:
  Articles and blogs on checkpointing and experiment tracking (MLflow, Weights & Biases, etc.).

Conclusion

The message "Agent stopped due to max iterations" is an indicator, not an exhaustive diagnosis. The correct response depends on the context: sometimes it is an expected limit, other times it is a symptom of convergence issues, poor hyperparameters, or logic bugs. A consistent approach involves gathering logs and metrics, reproducing behavior on reduced data, adjusting limits reasonably, implementing early stopping and checkpoints, and covering the code with tests if a bug is suspected. The examples and links above will help you choose the right practical steps for your specific case.