Displaying OpenAI Gym simulations on a remote server

Other Questions OpenAI
1

I’m working on a Python script using OpenAI Gym on an AWS p2.xlarge instance. The problem is I can’t see the simulations when I try to render them. Here’s a basic example of what I’m trying to do:

import gym
env = gym.make('MountainCar-v0')
env.reset()
env.render()

When I run this, I get errors about OpenGL and virtual frame buffers. I’ve tried a few things like using xvfb-run, but nothing seems to work. I’m getting different errors like ‘GLXInfoException’ or ‘ImportError: cannot import name gl_info’.

I know the issue is related to not having a display on the server, but I’m not sure how to work around it. Is there a way to see these simulations remotely? Or maybe save them as video files?

I’ve looked into some solutions using bumblebee, but that doesn’t seem to work with AWS. At this point, I’m considering just creating my own simple rendering mechanism for the environments I need. Any ideas or suggestions would be really helpful!

2

hey there! i’ve dealt with this before. try using ‘gym.wrappers.Monitor’ to save videos of ur simulations. it’s super easy:

env = gym.make(‘MountainCar-v0’)
env = gym.wrappers.Monitor(env, ‘./videos’, force=True)

this’ll save episodes as mp4s in a ‘videos’ folder. u can download & watch em later. no display needed!

3

For remote rendering with OpenAI Gym, consider using VNC or X11 forwarding. Set up a VNC server on your AWS instance and connect to it from your local machine. This allows you to view the graphical output remotely. Alternatively, you could modify your code to save frames as images or video files directly on the server. Use env.render(mode=‘rgb_array’) to get frame data, then use a library like OpenCV or PIL to save these frames. You can then compile them into a video using ffmpeg. This approach avoids display-related errors and gives you a permanent record of your simulations for later analysis.

4

Having worked with OpenAI Gym on remote servers, I’ve found a reliable solution to this common issue. Instead of attempting real-time rendering, consider using the ‘rgb_array’ mode to capture frames programmatically. Here’s an approach that’s worked well for me:

import gym
import imageio

env = gym.make(‘MountainCar-v0’)
frames =
obs = env.reset()

for _ in range(1000): # Adjust based on your needs
action = env.action_space.sample() # Replace with your agent’s logic
obs, reward, done, info = env.step(action)
frames.append(env.render(mode=‘rgb_array’))
if done:
break

imageio.mimsave(‘simulation.gif’, frames, fps=30)

This method saves the entire simulation as a GIF, which you can easily transfer and view locally. It bypasses display-related errors and provides a compact, shareable visualization of your agent’s performance.

5

As someone who’s worked extensively with OpenAI Gym on remote servers, I can relate to your frustration. One solution that’s worked well for me is using a headless rendering approach. Instead of trying to display the simulation in real-time, you can render frames to a virtual buffer and save them as images or video.

Here’s a snippet that might help:

import gym
import numpy as np
from gym.wrappers import Monitor

env = gym.make('MountainCar-v0')
env = Monitor(env, './video', force=True)

observation = env.reset()
done = False
while not done:
    action = env.action_space.sample()  # Replace with your agent's action
    observation, reward, done, info = env.step(action)

env.close()

This approach uses gym.wrappers.Monitor to automatically save episode renders as video files. You can then download and view these locally. It’s not real-time, but it’s reliable and doesn’t require complex server setups. Plus, it’s great for debugging and showcasing your work later on.

6

hey, i had similar issues. try using ‘xvfb-run -a python your_script.py’. it creates a virtual framebuffer. if that doesn’t work, you could save frames as images using env.render(mode=‘rgb_array’) and stitch them into a video later. hope this helps!