Coinbase India: Everything about regulation

Introduction

The message "Agent stopped due to max iterations" means only one thing: the process (training, optimization, or simulation) was forcibly stopped upon reaching a predefined iteration limit. This is not a diagnosis—it is a signal to understand why the limit was triggered and what to do next. Below is a concise breakdown: theory vs. practice.

Theoretical Part — What, Where, and Why

What it means

• The process terminated because a predefined limit of iterations/epochs/steps was exhausted, while other stopping criteria (convergence, reaching a quality threshold) were not met.

Where it occurs (briefly)

• Neural network training (epochs/batch iterations).
• Reinforcement Learning (episodes/steps per episode).
• Numerical methods and optimization (iterative methods).
• Agent-based simulations.

Main causes

• Iteration limit is set too low.
• Slow or absent convergence due to hyperparameters, poor data, or incorrect problem formulation.
• Implementation errors (bugs in updates, agent logic, or data processing).
• Unsuitable reward function or quality metrics (in RL).

Practical Part — Diagnostics and Actions

Diagnostics (what to check first)

• Review the logs: analyze loss/reward curves over iterations.
• Evaluate gradient dynamics and the magnitude of updates.
• Reproduce the issue on a simplified task or a subset of the data.
• Enable detailed logging/checkpoints to identify exactly where the process stalls.

How to act (specific steps)

• Increase the iteration limit if resources allow to rule out artificial interruption.
• Implement flexible stopping criteria: early stopping based on validation, time-based timeouts, or improvement thresholds.
• Fine-tune hyperparameters: learning rate, optimizer, regularization.
• Improve data quality: cleaning, normalization, class balancing, or data augmentation.
• In RL: rethink the reward function and exploration strategy; use stability techniques (target networks, experience replay).
• Debug the code: run unit tests, check data flows, and visualize agent behavior.

Practical Recommendations (briefly)

• Always log metrics and save checkpoints.
• Start with small-scale tests and gradually increase limits.
• Document the chosen limits and the reasons for setting them.
• Use combined stopping criteria.

Conclusion

The message about reaching maximum iterations is a call to investigate, not a final explanation. Divide your analysis into a theoretical check (why the process did not converge) and practical actions (diagnostics, adjusting limits and hyperparameters, code debugging). Only then can you make an informed decision: whether to increase the limit, change training settings, or redesign the task entirely.