Rl Reward Circuit Mixed Optimization

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/rl_reward_circuit_mixed_optimizationThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Implements a reinforcement learning reward function for analog circuit design that handles mixed minimization/maximization objectives using normalized differences, while incorporating transistor saturation tracking to…

---
id: "364f2105-6445-41ec-8b4b-84727d396a2a"
name: "rl_reward_circuit_mixed_optimization"
description: "Implements a reinforcement learning reward function for analog circuit design that handles mixed minimization/maximization objectives using normalized differences, while incorporating transistor saturation tracking to ensure stability."
version: "0.1.3"
tags:
  - "RL"
  - "reward function"
  - "circuit optimization"
  - "mixed objectives"
  - "transistor saturation"
  - "python"
  - "self"
  - "policy"
  - "action"
triggers:
  - "calculate reward function for reinforcement learning"
  - "RL reward condition with normalized difference"
  - "mixed optimization reward calculation"
  - "reward logic for transistor saturation"
  - "implement circuit environment reward function"
  - "Use when the user asks for a process or checklist."
  - "Use when you want to reuse a previously mentioned method/SOP."
examples:
  - input: "Break this into best-practice, executable steps."
---

# rl_reward_circuit_mixed_optimization

Implements a reinforcement learning reward function for analog circuit design that handles mixed minimization/maximization objectives using normalized differences, while incorporating transistor saturation tracking to ensure stability.

## Prompt

Follow this SOP (replace specifics with placeholders like <PROJECT>/<ENV>/<VERSION>):
1) Offline OpenAI-format conversation source.
2) Title: ae325e3c206735467f9648f983310cdd.json#conv_1
3) Use the user questions below as the PRIMARY extraction evidence.
4) Use the full conversation below as SECONDARY context reference.
5) In the full conversation section, assistant/model replies are reference-only and not skill evidence.
6) Primary User Questions (main evidence):
7) My Objective:
8) We aim to optimize the '7' objective performance metric values of 'area', 'power dissipation', 'DC gain', 'Slew rate', '3db frequency', 'Unity gain bandwidth', and 'Phase margin' (multi-objectives) of a pre-determined two stage operational amplifier circuit topology. We need to optimally configure/tune the design device parameters (device dimensions and values, as variables) such as transistor dimensions ['L1', 'L3', 'L5', 'L6', 'L7', 'W1', 'W3', 'W5', 'W6', 'W7'], Bias currents ['Ib'], Bias voltages ['Vb'], and values of the passive devices ['Cc', 'Cl', 'Rp'] associated in the circuit design, in a such a way that the resulting design satisfies the predetermined performance measures (design specifications). In this overall construction of optimization process, the pre-determined performance measures are considered as constraints as well as target optimization metrics to configure the optimized design device parameter values.
9) For my objectives, I need to implement the reinforcement learning (RL) code with multiple output heads Graph neural networks on multigraphs, GNN is embedded by the technique of hybrid MPNN-RGAT architecture that contains the message passing attention mechanism. Here GNN is with in the agent the action and rewards are performed from the following requirement, for this requirement suggest me the best optimization techniques that take actions and generate rewards and to update the parameters.
10) Environment: My environment is the Cadence virtuoso simulator, where I am simulating the pre-designed fixed circuit topology. I am accessing the simulator available in the local server through the OCEAN skill command controlled by python programming. The environment gets the input variables for the design device parameters ['L1', 'L3', 'L5', 'L6', 'L7', 'W1', 'W3', 'W5', 'W6', 'W7', 'Ib', 'Cc'] from the agent and gives the output performance metrics ['area', 'power dissipation', 'DC gain', 'Slew rate', '3db frequency', 'Unity gain bandwidth', 'Phase margin'] corresponding to the given input variables. Circuit topology is fixed, only the design device parameters present in the schematic are variables to be optimally configure to met the desired output performance metrics.

For each step, include: action, checks, and failure rollback/fallback plan.
Output format: for each step number, provide status/result and what to do next.

## Triggers

- calculate reward function for reinforcement learning
- RL reward condition with normalized difference
- mixed optimization reward calculation
- reward logic for transistor saturation
- implement circuit environment reward function
- Use when the user asks for a process or checklist.
- Use when you want to reuse a previously mentioned method/SOP.

## Examples

### Example 1

Input:

  Break this into best-practice, executable steps.

Excerpt, first 4,598 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Rl Reward Circuit Mixed Optimization

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

Spotted something wrong? Report a correction · Methodology