Check whether the problem needs reinforcement learning
Reinforcement learning learns a policy for choosing actions through interaction with an environment and a reward signal. Consider it when actions affect future states and the task requires decisions over time. A static prediction problem may be better served by supervised learning; an allocation problem with known constraints may have a useful optimization baseline.
Specify the operational decision, available observations, action frequency and acceptable outcomes. Identify how interactions can be obtained and what each experiment costs. Simulation or historical data does not automatically make the decision problem suitable for RL. Compare the proposed approach with the existing controller, heuristic or planning method before committing to a training program.
Define and validate the environment contract
Document observations, actions, transition behavior, rewards and episode boundaries. Include units, limits, delays and missing-sensor behavior. The policy should receive information available at the decision time, not privileged future data from the simulator. Record preprocessing and the transformation from a policy output to an executed action.
Distinguish a task’s terminal state from an artificial episode time limit. Test reset behavior and known transitions in an approved test environment. Validate the environment with simple scripted policies before training. For a physical system, experimentation needs the appropriate operational controls; a simulator test should not be implemented as uncontrolled exploration on equipment.
Separate reward from the real objective
Write down the desired outcome and constraints independently of the training reward. A reward is a chosen signal, not proof that the policy meets the operational objective. Inspect whether an agent can earn reward by exploiting a simulator artifact, repeatedly triggering a bonus or avoiding episode completion.
For example, a warehouse policy rewarded only for completed moves might choose frequent short moves while delaying an urgent order. Evaluate order completion, lateness and constraint violations separately from accumulated reward. This is a hypothetical design example, not a reported warehouse result. Review changes to reward weights with the task owner and test the resulting behavior.
Compare algorithms under a reproducible budget
Choose candidates according to the action space, interaction budget and environment characteristics. Record the library version, configuration, random seeds, training steps and compute used. An algorithm that performs well on one benchmark is not a universal choice for a robot, game or resource-management workflow.
The official Stable-Baselines3 experiment guidance emphasizes separate evaluation environments, attention to wrappers and multiple training runs. It also describes limitations including sample inefficiency and unstable training. Use those principles to design an experiment rather than treating default settings as a production guarantee.
Give the baseline and candidate policies comparable evaluation conditions. Report interaction count and wall-clock cost as well as performance. Preserve experiment configurations so a promising result can be reproduced before selecting a release.
Evaluate held-out conditions and failures
Keep evaluation conditions separate from tuning. Test the policy on relevant initial states, demand patterns, disturbances and delayed observations. Record whether evaluation uses a deterministic policy or a stochastic policy and keep the choice consistent when comparing candidates.
Report variation across runs, task success, constraint violations, episode length and failure cases alongside reward. A high mean can hide unacceptable rare outcomes. Check that evaluation wrappers or normalization do not change the task’s meaning. Explain how many runs and episodes support a conclusion and where the environment does not represent deployment conditions.
Release a bounded policy and measure operating value
Investigate differences between simulated and operational behavior, including sensor noise, actuation delay and unmodeled constraints. Expand a pilot only after the responsible owner approves its scope. Define action limits, a fallback controller, monitoring and a stop or rollback procedure before enabling operational actions.
Version the policy, preprocessing, environment and deployment configuration. Monitor actual executed actions and outcomes, not only requested actions. Re-evaluate changes before rollout and document incidents where the fallback is used. Simulation gains should remain labeled as simulation results until the operational outcome has been measured.
Budget environment construction, data, simulation, training, evaluation, integration and recurring operations from the actual workload. Use the MLOps guide for release controls, explore AI project scoping or discuss a bounded RL feasibility study.