Spinning Up Deep Rl
Spinning Up Deep Rl: Study a reinforcement learning concept through implementation. Review the target algorithm, environment, baseline, and evaluation metric and produce an RL learning plan with experiments.
--- name: engineering-spinning-up-deep-rl description: Use for spinning up deep rl when asked to study a reinforcement learning concept through implementation; produce an RL learning plan with experiments. license: MIT metadata: author: Thrive category: engineering --- # Spinning Up Deep Rl ## When to use Use this skill for spinning up deep rl when you need to study a reinforcement learning concept through implementation. The expected result is an RL learning plan with experiments. ## Boundaries Work within the requested task and its stated acceptance criteria. Drafting an artifact does not authorize publishing it, spending funds, changing a live system, or contacting another person. Identify any such action separately before taking it. ## Inputs Inspect the target algorithm, environment, baseline, and evaluation metric. Resolve missing information that would change the method; state lesser assumptions in the result. ## Method 1. **Diagnose.** Identify the runtime boundary, data shape, dependencies, deployment path, and the constraint that dominates the design. 2. **Decide.** Connect the mathematical objective to the code and test one assumption at a time. 3. **Produce.** Build an RL learning plan with experiments from the inspected material; keep assumptions distinguishable from observed facts. ## Decision rules - Compare implementation choices on correctness, compatibility, performance, operability, and reversibility. Choose against the actual workload. - When sources or constraints conflict, record the conflict and choose the path supported by the user's goal and the strongest available evidence. If neither path can be supported, identify the missing decision before changing the artifact. ## Domain rules - Check compatibility, performance, security, and operational cost for the chosen implementation. - Include migration and rollback steps when contracts or persisted data change. ## Verification Compare training behavior with a reproducible baseline. Compare the result with the user's acceptance criteria and record any unverified boundary. Supply the artifact, migration notes when needed, measured or tested behavior, and a rollback condition for risky changes. ## Stop conditions If a material input, required authorization, or a safe way to verify the result is absent, stop the affected action. Return the specific blocker and the smallest fact or decision needed to continue. Do not report an unrun check as passed. ## Output Provide an RL learning plan with experiments. Include the decisive evidence and actual verification result. Name any artifact location and unresolved issue that affects its use. <!-- MIT License Copyright (c) 2026 Thrive Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -->