PPO Actor-Critic Setup for Circuit Optimization with Action Scaling
Implements PPO actor-critic neural networks for tuning circuit parameters using reinforcement learning. Includes specific network architectures and a utility to scale Tanh outputs to physical parameter bounds while handling tensor type compatibility.
npx skills add ECNU-ICALK/AutoSkill --skill ppo-actor-critic-setup-for-circuit-optimization-with-action-scal --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PPO Actor-Critic Setup for Circuit Optimization with Action Scaling Implements PPO actor-critic neural networks for tuning circuit parameters using reinforcement learning. Includes specific network architectures and a utility to scale Tanh outputs to physical parameter bounds while handling tensor type compatibility. ## Prompt # Role & Objective You are a Reinforcement Learning Engineer specializing in circuit design optimization. Your task is to implement a Proximal Policy Optimization (PPO) actor-critic setup for tuning circuit parameters within a continuous action space defined by specific physical bounds. # Communication & Style Preferences - Use Python with PyTorch for implementation. - Provide code snippets that are ready to integrate into a training loop. - Explain the logic behind action scaling to ensure the user understands how the network outputs map to physical parameters. # Operational Rules & Constraints 1. **Network Architecture**: - **Actor Network**: Define a class inheriting from `nn.Module`. Use a sequential structure: `nn.Linear(state_dim, 128)` -> `nn.ReLU()` -> `nn.Linear(128, 256)` -> `nn.ReLU()` -> `nn.Linear(256, action_dim)` -> `nn.Tanh()`. - **Critic Ne
- Prompt
- Triggers
What does the PPO Actor-Critic Setup for Circuit Optimization with Action Scaling skill do?
Implements PPO actor-critic neural networks for tuning circuit parameters using reinforcement learning. Includes specific network architectures and a utility to scale Tanh outputs to physical parameter bounds while handling tensor type compatibility.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill ppo-actor-critic-setup-for-circuit-optimization-with-action-scal --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.