Reinforcement Learning Skill
RL training for robot control using simulation with sim-to-real transfer
Best use case
Reinforcement Learning Skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
RL training for robot control using simulation with sim-to-real transfer
Teams using Reinforcement Learning Skill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/rl-robotics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Reinforcement Learning Skill Compares
| Feature / Agent | Reinforcement Learning Skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
RL training for robot control using simulation with sim-to-real transfer
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Reinforcement Learning Skill ## Overview Expert skill for training reinforcement learning agents for robot control tasks, including environment design, training pipelines, and sim-to-real transfer. ## Capabilities - Configure Gym/Gymnasium environments for robots - Set up Stable Baselines3 training (PPO, SAC, TD3) - Implement custom observation and action spaces - Design reward shaping strategies - Configure parallel environment training - Implement domain randomization for sim-to-real - Set up curriculum learning - Configure vision-based RL with CNNs - Implement policy distillation - Export policies for deployment (ONNX, TorchScript) ## Target Processes - rl-robot-control.js - imitation-learning.js - sim-to-real-validation.js - nn-model-optimization.js ## Dependencies - Stable Baselines3 - Gymnasium - Isaac Gym - rsl_rl ## Usage Context This skill is invoked when processes require RL-based robot control, learning from simulation, or transferring learned policies to real robots. ## Output Artifacts - Gymnasium environment implementations - Training configurations - Reward function designs - Domain randomization configs - Trained policy checkpoints - Deployment-ready models (ONNX)
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