| name | evaluating-simulator-feasibility |
| description | This skill evaluates whether a fast, safe, and accurate simulation environment can be built for the agent to train in. |
| version | 0.1.0 |
Evaluating Simulator Feasibility
RL requires millions of trial-and-error attempts to converge. An agent cannot train on live, physical, or financially critical systems without causing catastrophic damage. This skill assesses the viability of constructing the required "Gym".
Step 1 — Analyze Physical vs. Digital
Is the environment a physical robot/process, or a pure software/data construct?
- Physical: Requires integration with Physics Engines (Isaac Sim, MuJoCo). If the physics are too complex to model accurately (e.g. fluid dynamics, soft robotics), the Sim-to-Real gap might be fatal.
- Digital/Financial: Can we replay historical data fast enough? Does our historical data contain counter-factuals (what happens if the agent buys when historically we sold?)
Step 2 — Verify Speed Requirements
An RL simulator must step extraordinarily fast.
- If the environment takes 1 second per step to calculate, training an agent for 10 million steps will take ~115 days.
- Requirement: The simulation must realistically support >1000 steps per second via vectorization/C++ backing.
Step 3 — Fallback to Offline RL
If a simulator is impossible to build or too slow:
- We cannot use standard Online RL (PPO/SAC).
- We must evaluate if we possess massive, pre-recorded datasets required to perform Offline RL (Conservative Q-Learning, Decision Transformers).
Output Format
Generate the Simulator Viability Report:
Simulator Feasibility:
Environment_Type: [Physical / Digital / Financial]
Estimated_Step_Latency_ms: [Value]
Sim_to_Real_Risk: [Low / Med / High]
Simulation_Viable: [True / False]
Fallback_To_Offline_RL_Required: [True / False]
