# Reinforcement Learning

Kangaroo's locomotion controllers are trained using reinforcement learning (RL) rather than hand-tuned by hand. A policy is trained in simulation to map robot observations (joint states, IMU, commanded velocity, etc.) to joint commands, and is then deployed on the real robot.

## mjlab

Training is done with **mjlab**, a GPU-accelerated RL framework built on top of [MuJoCo](../simulation/mujoco.md). It follows the same environment/task API popularised by NVIDIA Isaac Lab, but runs on MuJoCo's physics engine instead of Isaac Sim. This lets thousands of environments be simulated in parallel on a single GPU, which is what makes training locomotion policies from scratch practical.

Because Kangaroo is also simulated with MuJoCo (see [MuJoCo Simulation](../simulation/mujoco.md)), a policy trained in mjlab uses the same physics and contact model it will later run against in simulation, minimising the sim-to-sim gap before moving to the real robot.

## pal_mjlab

Kangaroo-specific mjlab environments — rewards, observations, terrain, and domain randomization tailored to the robot — live in the [`pal_mjlab`](https://github.com/pal-robotics/pal_mjlab) repository. Start there for training or modifying locomotion policies for Kangaroo; its README covers setup and usage.