What should an outsourced reinforcement learning project using software simulation deliver?

Last updated:2026-09-30 · New Horizon Digital Outsourcing Team

Simulation-based reinforcement learning requires more than a video of robot motion. Deliver a reproducible environment, observation and action definitions, reward and termination conditions, training configurations, policy checkpoints, and independent evaluation results. A policy that works in simulation is not automatically safe for controlling real hardware.

What does training in software simulation involve?

Define tasks, observable states, executable actions, and feedback signals in the environment so policies can learn through repeated interaction. Frameworks such as Isaac Lab provide infrastructure for simulation-based learning, but project-specific tasks, model parameters, and rewards still require implementation and inspection.
Model fidelity should serve task objectives. For tasks sensitive to contact, actuators, sensors, or communication delays, explain the relevant approximations rather than using rendering quality as a substitute for physical and control model quality.

Why does increasing reward not mean the task is complete?

A policy may earn rewards through unintended behavior. Beyond return, evaluation should check task success rates, constraint violations, abnormal termination, and recovery capabilities, and replay failures to identify causes.
Separate training and evaluation scenarios, and record randomization ranges, random seeds, and checkpoints. Do not select only one successful replay or repeatedly alter test conditions after seeing test results.

How should compute resources, schedules, and delivery scope be estimated?

Use a small pilot to measure time per step, the number of parallel environments, and training throughput before assessing the benefits of scaling up. GPU memory, rendering frequency, and physics complexity all affect results; training time should not be promised based on GPU model alone.
List environment development, training experiments, evaluation analysis, and deployment adaptation separately in the contract. Also agree separately on source code, model weights, dependency licenses, and hardware requirements.

Boundaries of transfer from simulation to hardware

Before transferring to hardware, reassess model discrepancies, observation noise, action limits, and failure protection. Published training results on other tasks can inform methods, but cannot be copied into success-rate commitments for a new project.
If this phase accepts only software simulation, explicitly exclude guarantees of real-hardware performance. Handing over evaluation script entry points, fixed benchmarks, and failure cases supports maintenance better than delivering final weights alone.