Illegal mining farms: impact and solutions

Introduction

The message "Agent stopped due to max iterations" is commonly encountered in systems where behavior is driven by iterative algorithms: optimizers, search agents, reinforcement learning, simulators, and more. By itself, this notification is not very informative: it simply indicates that the agent finished its work not because it reached a target metric or met a success condition, but because the iteration limit was exhausted. In this article, we will examine what such a termination means, why it can be problematic or normal in different contexts, and what steps to take for diagnosis and correction.

Main Body

1. What "max iterations" means

• Iteration threshold — a software-defined limit on the number of cycles (episodes, steps) after which the process is forcibly stopped to avoid infinite loops or excessive resource consumption.
• In different systems, this can represent: the number of epochs in model training, the number of agent planning steps, iterations of a numerical method, etc.

2. Why an agent might stop due to the iteration limit

• The algorithm did not reach the convergence criterion (e.g., target accuracy or error reduction).
• Incorrectly configured hyperparameters (too strict a threshold or too low a learning rate).
• Task complexity exceeds allocated resources (insufficient search depth, small iteration buffer).
• Bugs in the stopping logic: the success criterion is never checked or is constantly reset.

3. How to diagnose the cause

• Review the logs: are significant changes in metrics (error, reward) recorded during the process? If metrics consistently fail to improve, the problem lies in the algorithm or hyperparameters.
• Enable more detailed logging: record metric values per iteration, time per iteration, and specific exit reasons.
• Reproduce the case with smaller datasets: if the agent converges on simple examples, the problem is likely task complexity.
• Check stopping conditions in the code: are success criteria being evaluated correctly?

4. Potential solutions and best practices

• Increase max iterations: a quick pilot step, but not a panacea — resource consumption will increase.
• Re-evaluate convergence criteria: make them more relevant to the task (e.g., using relative metric changes instead of absolute ones).
• Tune hyperparameters: adjust learning rate, regularization coefficients, or search parameters.
• Add adaptive stopping: implement early stopping if there is no improvement over N iterations.
• Introduce checkpoints and progress control: save intermediate models or states to avoid losing progress when increasing limits.
• Improve diagnostics: use metrics, training/search visualization, and resource intensity tracking.
• Optimize the algorithm: utilize more efficient methods, approximations, or parallelization.

5. Example practical action plan

• Step 1: Enable detailed logging of metrics for every iteration.
• Step 2: Run a test on a simplified task and compare metric dynamics.
• Step 3: If necessary, increase the limit and add early stopping based on the lack of improvement.
• Step 4: If metrics still do not improve, investigate hyperparameters or model architecture.
• Step 5: Repeat the process and lock in the working configurations.

Conclusion

The message "Agent stopped due to max iterations" is a symptom, not a diagnosis. It indicates that the process was interrupted by an iteration limit but does not explain whether an optimum was reached or if the process stagnated. To make informed decisions, diagnosis is required: monitoring metrics, verifying stopping conditions, and adjusting hyperparameters or resources. Simple steps — such as more detailed logging, tests on simplified data, and implementing adaptive stopping strategies — will help you quickly understand and resolve the underlying cause.