Crypto regulation trends 2023

Title: The "Agent stopped due to max iterations" message — what it means and how to handle it

Introduction

The "Agent stopped due to max iterations" message is usually not an error description in the conventional sense, but a notification that the agent's execution has been halted because the iteration limit was reached. This behavior is typical for learning and optimization algorithms (e.g., gradient descent, reinforcement learning), simulations, and search procedures, where a limit on the number of iterations is introduced to prevent infinite loops or excessive resource consumption.

Causes

The main reasons for this message appearing are:

• The maximum number of iterations is set intentionally (protection against infinite loops or timeouts).
• The algorithm did not reach the convergence criterion before the set limit (insufficient convergence speed or poor initialization).
• Incorrect hyperparameters (step size too large, low learning rate, etc.).
• Implementation bugs resulting in a lack of progress.

How to Diagnose

1. Review logs and metrics (objective function value, loss, reward) by iteration — is there any visible improvement?
2. Check the stopping criteria: it might be worth using a convergence check based on metric changes rather than just the number of iterations.
3. Check input data and initial conditions — non-functioning normalization or poor initialization can hinder progress.
4. Run a debug pass with smaller data or step-by-step to ensure the algorithm is performing the predicted updates.

Recommendations

• Increase the iteration limit only after analysis: this is a temporary fix, not a root cause solution.
• Set up adaptive stopping criteria (early stopping based on validation, metric change threshold).
• Adjust hyperparameters (learning rate, batch size) and regularization methods.
• Cover critical sections with tests and add more detailed log messages.

Conclusion

The "Agent stopped due to max iterations" message is a useful indicator that execution was halted by a limit. Instead of ignoring it, it is worth conducting a systematic diagnosis and, if necessary, adjusting stopping criteria, hyperparameters, or code to ensure stable and efficient agent performance.