rl-isaaclab

What problem does it solve?

This Skill streamlines the entire Reinforcement Learning workflow within the IsaacLab environment, from local testing to cluster deployment and result synchronization.

Core Features & Use Cases

• Local Smoke Testing: Quickly validate RL scripts on a smaller scale before cluster submission.
• Cluster Job Submission & Monitoring: Deploy and manage RL training jobs on the Euler cluster.
• Result Synchronization: Consolidate experiment results from the cluster.
• Policy Playback: Locally run trained policies for evaluation.
• Use Case: Train a robot arm policy for a manipulation task, starting with a quick local test, submitting a large-scale training run to the cluster, monitoring its progress, and finally playing back the learned policy to visualize its performance.

Quick Start

Run a local smoke test for the excavation3d_w_cabin task with 4 environments for 3 iterations.