I’ve been using the Hopper setup in the OpenAI Gym and noticed something puzzling. The joint velocity observations that are given to the agent are clipped, yet the actual state values can go beyond these limits.
This seems odd because one would expect the observations to reflect the real state values. I’m curious if there’s a particular reason for this design or if it could be an oversight.
Has anyone else seen this behavior? I would love to hear thoughts on whether this is intended or if it should be flagged as a possible issue.
yeah, this threw me off too when i was working on walker environments. it’s a design choice - they want to keep observations from exploding during training while keeping the physics realistic. the sim needs real velocities for accurate dynamics, but agents perform better with bounded inputs.
This completely threw me off during my thesis on locomotion control. The clipping keeps things numerically stable during policy optimization. When joint velocities go crazy high, they cause gradient explosions that totally wreck the learning process.
What’s really cool is how the environment handles two separate data streams - the physics sim needs the raw values to calculate realistic dynamics and contact forces, but the agent gets clipped observations so it can learn consistently. This setup prevents those nasty cases where extreme observations early on mess up the policy initialization.
I figured this out while debugging why my custom reward functions didn’t match the physics behavior I expected. The environment calculated rewards using the true state values, but my agent only saw the clipped versions. This created a mismatch in my evaluation metrics. Once I understood this, I got way better at designing observation preprocessing for other continuous control tasks.
This is actually intentional - it’s how MuJoCo handles observation preprocessing in gym environments. The clipping normalizes observations to prevent extreme values from breaking learning algorithms, especially policy gradient methods that are sensitive to input scaling. I ran into this exact issue with continuous control tasks and thought it was a bug at first. But after digging through the MuJoCo docs, I found out the real state values stay unclipped for physics simulation - only the observations get processed for training stability. The environment keeps things consistent by tracking the actual physics state separately from what the agent sees. If you need the raw velocity values, you can modify the observation wrapper or grab them directly from the sim object.
Gym environments clip values to keep training stable, not to give you accurate raw data. I ran into this exact issue on a robotics project and it was super frustrating at first.
RL algorithms need observations within reasonable bounds. Without clipping, you get massive velocity spikes that completely wreck neural network training. Environment designers keep the physics sim running with true values but feed agents normalized observations.
Here’s what I did - built a monitoring system that tracks both clipped and raw values in real time. Instead of hacking gym wrappers or diving into MuJoCo internals, I automated the whole data pipeline to capture, process, and log everything.
My solution pulls raw state data, applies custom preprocessing, and feeds clean observations to multiple training runs at once. It also generates reports comparing how clipping affects learning curves.
I use Latenode for this kind of automation workflow now. You can set up the entire pipeline - data extraction, preprocessing, logging - without writing complex integration code. It handles all the API calls and data transformations automatically.