Tutorial on WorldSense Tech Blog

Isaac Lab Installation Guide: From Zero to Running on AutoDL

Sat, 15 Aug 2026 00:00:00 +0000

The previous article covered what Isaac Lab is and what it can do. But if you’ve actually tried installing it, you know there’s quite a gap between “cloning the repo” and “running the examples.”

This article documents my complete process of installing Isaac Lab on an AutoDL cloud GPU server (RTX 5090D / 32GB). Every error mentioned here was genuinely encountered, and every solution was verified in practice. Hopefully this saves you a few hours of debugging.

DreamerV3 Training Tips: Lessons from Real-World Debugging

Tue, 11 Aug 2026 00:00:00 +0000

In the previous article, we walked through four representation approaches for world models. Today, we shift back to the practical side of DreamerV3 and talk about the pitfalls and tricks you encounter during training. This article is based on local experiments using DreamerV3 commit e3f02248, JAX + Haiku, and MuJoCo + DM Control. Parameter names and configurations may differ across versions.

DreamerV3 is currently one of the most open-source and mature world model implementations available. But if you’ve actually trained it, you know the process is far from easy — environment setup, hyperparameter tuning, training instability, slow convergence… the list of gotchas goes on.

How to Get Started with Reinforcement Learning: A Practical Guide

Mon, 03 Aug 2026 00:00:00 +0000

I have a deep appreciation for this question. I transitioned from traditional automation into reinforcement learning myself, and I stumbled through plenty of pitfalls along the way. Here’s the path I’ve found most effective.

First Things First: What Do You Want to Do with Reinforcement Learning?

Reinforcement learning spans a wide range of application domains, and the learning path differs for each:

Games and simulation: Atari games, MuJoCo robot simulations — the most beginner-friendly with the most resources available.
Robotics control: Robotic arms, quadruped robots, humanoid robots — requires combining simulation with Sim-to-Real transfer.
Recommender systems and advertising: The primary application scenario for internet companies — more engineering-focused, with relatively lower math requirements.
Autonomous driving: Decision-making and planning modules — requires integration with classical control theory.

Decide on your direction first, then choose your learning path accordingly — it makes a huge difference in efficiency. The advice below follows “robotics control” as the main thread, since that’s the area I know best and one of the most promising directions today.