Visualization

To make it easy for you to create high quality and interesting demonstrations of AIs, or be able to analyse behaviour of trained agents, Griddly provides many tools to make this easy.

Live Rendering

OpenAI gym allows gym environments to be rendered using the env.render() function. However in many Griddly environments you may have a choice between different players and global observations to render.

Rendering Different Observers

To render global observations you simply need to add the parameter observer='global' to your render function. Additionally if you want to render a particular player you can use observer=P where P is the 0-indexed player id.

env.render(observer='global') # Will render the global observer

env.render(observer=0) # Will render the observer for player 1

env.render(observer=1) # Will render the observer for player 2

Player Highlighting

In games with multiple players, Griddly will highlight all the objects in the environment based on which observer is being used.

The global observer will highlight player owned objects with colours which are automatically picked
The player observer will highlight objects belonging to themselves in green

Observer Highlighting
Global	Player 1	Player 2

Saving Images

Griddly includes a tool RenderToFile that can directly save observations to disk as png files.

from griddly.RenderTools import RenderToFile

render_to_file = RenderToFile()

...

visualization = env.render(observer=.., mode='rgb_array') # Get the observation as an array

render_to_file.render(visualization, 'my_observation.png') # save the image to disk

Saving Videos

Also saving videos with Griddly is simple using the VideoRecorder.

Note

you will need to make sure ffmpeg is installed in your system.

from griddly.RenderTools import VideoRecorder

video_recorder = VideoRecorder()

...

# Start the video recording
observation = env.reset()
video_recorder.start("video_test.mp4", env.observation_space.shape)

...

# Step the environment and record the next frame
obs, reward, done, env = env.step( ... )
video_recorder.add_frame(obs)

...

# Clean up
video_recorder.close()