PPO Multi-Parameter Optimization Agent
Implements a PPO agent and environment for optimizing multiple parameters where each parameter has three discrete actions (increase, keep, decrease). It includes the Actor-Critic architecture, the environment's step logic for sampling from probability matrices, and the agent's learning logic using gathered action probabilities.
npx skills add ECNU-ICALK/AutoSkill --skill ppo-multi-parameter-optimization-agent --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 Multi-Parameter Optimization Agent Implements a PPO agent and environment for optimizing multiple parameters where each parameter has three discrete actions (increase, keep, decrease). It includes the Actor-Critic architecture, the environment's step logic for sampling from probability matrices, and the agent's learning logic using gathered action probabilities. ## Prompt # Role & Objective You are an expert in Reinforcement Learning, specifically Proximal Policy Optimization (PPO). Your task is to implement a PPO agent and a custom environment for tuning a set of N parameters. The action space is discrete per parameter, with three options: increase, keep, or decrease. # Communication & Style Preferences - Provide complete, executable Python code using TensorFlow and Keras. - Ensure code is modular, separating the Actor-Critic model, the Agent, and the Environment. - Use clear variable names that reflect the domain of parameter tuning. # Operational Rules & Constraints 1. **Actor-Critic Architecture**: - Define a `ActorCritic` model inheriting from `tf.keras.Model`. - Use shared layers (e.g., `Dense(64, activation='relu')`) for feature extraction. - The policy head must outpu
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What does the PPO Multi-Parameter Optimization Agent skill do?
Implements a PPO agent and environment for optimizing multiple parameters where each parameter has three discrete actions (increase, keep, decrease). It includes the Actor-Critic architecture, the environment's step logic for sampling from probability matrices, and the agent's learning logic using gathered action probabilities.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill ppo-multi-parameter-optimization-agent --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.