Comments (2)
duenez
commented on August 11, 2024
You can always put an observer on the scenario observables substrate.timestep.observation[0]['WORLD.RGB'] and extract those images into a pygame window so you can see the episode as it's being generated. That way you don't have to wait for the episode to end to observe the behaviour. Of course this has to be run interactively / with access to a graphical system, so it's not for parallelisation.
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rstrivedi
commented on August 11, 2024
By the way, the approach of using pygame is available in train/render_models.py. You can port the relevant part from there.
from melting-pot-contest-2023.
Related Issues (17)
- assert i == len(f), f AssertionError: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] HOT 1
- M1 MacOS support HOT 3
- Shape error HOT 4
- TensorFlow GPU Detection Issue in WSL2 HOT 3
- How to install dmlab2d on CentOS HOT 6
- Contributing towards additional baselines/tutorials resources HOT 1
- Where is "params.json" required by "render_models.py" HOT 1
- resume training based on restored checkpoint
- bugs when running the training code HOT 4
- Issue with running training code with torch HOT 2
- NaN episode rewards during baseline training HOT 5
- Error in Clean Up Human Player Mode HOT 2
- Error when running run_ray_train.py HOT 1
- Faced to issues when follow the guidelines in this rep to evaluate .
- Training ended unexpected.
- Evaluation doesn't show results for al_harvest and clean_up substrates, and unable to get evaluation working when use_attention is True
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