---
name: reinforcement-learning-skill-34a3c93e3f85
description: RL training for robot control using simulation with sim-to-real transfer
slug: rl-robotics
category: Learning
allowed-tools:
  - Bash
  - Read
  - Write
  - Edit
  - Glob
  - Grep
graph:
  domains: [domain:robotics]
  specializations: [specialization:robotics-simulation]
  skillAreas: [skill-area:motion-planning, skill-area:sensor-fusion]
  roles: [role:research-engineer]
---

# 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)


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

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.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering
Roles: Backend Developer, Data Scientist, ML / AI Engineer
Specialties: Backend
Tools: Stable Baselines3
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 03f4fb155639d6f21deb22c6aa2a1984e014175be2a5c5d9eda61132552729f6