Cryptocurrency exchange security: how to protect yourself?

Introduction

The message "Agent stopped due to max iterations" looks like a technical log and is not very informative on its own. To turn this into a useful article, we need to explain exactly what this message means, the systems in which it appears, the problems it may signal, and the steps to take for diagnosis and resolution.

What the message means

This message typically appears in systems where an "agent" (an algorithm, process, simulation, or a trainable agent in machine learning) performs an iterative work cycle. Stopping upon reaching the maximum number of iterations means that the loop was forcibly terminated because a pre-set limit on the number of steps was reached, rather than because the algorithm naturally converged or completed the task.

Where it is encountered

• Optimization algorithms (gradient descent, evolutionary algorithms).
• Reinforcement learning and other types of machine learning.
• Agent-based simulations and multi-threaded services with iterative cycles.
• Search and planning algorithms (e.g., A*, MCTS), tests, and debug runs.

Possible causes

• The default max_iterations limit is too low for your specific task.
• The algorithm is not converging: the convergence stopping criterion is incorrect or unreachable.
• Loop logic error: the exit condition (break) is never met.
• Unsuitable hyperparameters (step size too large, incorrect reward, poor initialization).
• Dependency on randomness: different seeds or more repetitions are required.
• Incorrect data or environment state preventing progress.

How to diagnose

1. Review the logs: Which metrics were changing (loss function value, reward, distance to target)?
2. Increase logging within the iteration body: Periodic state dumps, parameters, and gradients.
3. Run with fewer iterations and detailed debugging to reproduce the issue.
4. Try a controlled example (toy dataset) where the expected behavior is known.
5. Test different seeds and hyperparameters to assess stability.

Practical solutions

• Increase max_iterations if the task simply requires more time.
• Revise stopping criteria: Add a check for metric changes over N iterations (early stopping) or an improvement threshold.
• Adjust hyperparameters: Decrease/increase the learning rate, change regularization.
• Improve initialization or input data normalization.
• Add gradient control (clipping) if divergence is occurring.
• Fix logic errors in the loop code.
• Implement checkpointing and the ability to resume training after a stop.
• If the problem lies in the environment (simulation), verify the correctness of its state and rules.

Engineer's Checklist

• Are there signs of metric improvement in the logs at the beginning? If not, the problem is in the model/data.
• Does the algorithm fail at the exact same step? If so, examine the input data at that step.
• Could the project benefit from an adaptive step (learning rate scheduler)?
• Is early stopping implemented based on a quality metric rather than just iterations?
• Is there time/resource control running in parallel with the iteration limit?

Conclusion

The message "Agent stopped due to max iterations" is a signal, not an explanation. A proper response requires determining why the iterations stopped: is the limit insufficient for the task, or is the algorithm not performing as expected? Use a systematic approach: logging, testing on simple examples, and tuning hyperparameters and stopping criteria. After this, you can either safely increase the iteration limit or fix the root cause preventing convergence.

If you need further assistance, please provide a description of the task, a log file, or key metrics, and I will suggest specific steps and configurations.