I’m having trouble getting the visual output to work when running OpenAI Gym environments on a GCP VM instance. I’m trying to test the MountainCar-v0 environment with this basic setup:
import gym
env_name = 'MountainCar-v0'
game_env = gym.make(env_name)
game_env.reset()
for step in range(500):
game_env.render()
action = game_env.action_space.sample()
game_env.step(action)
I have XQuartz set up and I’m connecting via ssh using the -X flag for X11 forwarding. Initially I got an error about pyglet not finding a display connection. After some research I found that using xvfb-run with screen settings helps, so I ran: xvfb-run -s "-screen 0 1024x768x24" /bin/bash
The code executes without errors and shows the expected gym output, but the render window never appears. Has anyone successfully got gym rendering working on Google Cloud VMs? What am I missing in my display configuration?
try VNC viewer instead of x11 forwarding - way more reliable for gym visuals. install a desktop on your VM (like xfce) then connect with a VNC client. x11 gets wonky with cloud latency anyway. or just render to images with plt.imshow(env.render('rgb_array')) and save frames manually if you need to debug what’s happening.
Had the same problem with RL on Azure VMs. The issue is xvfb creates a virtual display server-side, but X11 forwarding tries to send display data to your local machine - they don’t play nice together. I ditched the forwarding completely and used matplotlib instead. After each render, grab the frame with frame = env.render(mode='rgb_array') then use plt.imsave() for key frames or plt.imshow() in jupyter. You can also collect frames in a list and make animations with matplotlib’s animation functions. Way more reliable than fighting with display server configs on cloud instances.
Had this exact problem with RL projects on GCP. xvfb creates a virtual display but won’t forward it through X11 to your machine. Here’s what fixed it for me: set export DISPLAY=:99 before running your Python script, and start xvfb with Xvfb :99 -screen 0 1024x768x24 & first. Make sure XQuartz is running locally before you SSH in. Honestly though, I ended up switching to gym’s rgb_array render mode - just use game_env.render(mode='rgb_array') which gives you a numpy array you can save or process directly. Way easier than fighting with display forwarding.
This is super common with headless VMs. X11 forwarding breaks when you mix it with xvfb - the virtual framebuffer just doesn’t play nice with SSH forwarding. I’ve hit this exact issue running RL environments on cloud instances. Honest? Ditch the render window completely. Use gym.wrappers.Monitor instead - it saves videos straight to files. Just wrap your env like game_env = gym.wrappers.Monitor(env, './videos/', force=True) and it’ll auto-record episodes as MP4s you can download later. Way more practical for training since you don’t need live visuals anyway. If you absolutely need real-time viewing, grab a VM with a desktop environment and use VNC. Skip the X11 forwarding headache entirely.