49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
import tqdm
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import expert
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import copy
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import os
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import intersim
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from tqdm import tqdm
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def process_all_experts(filename='expert.pkl',env_args={}, policy_args={}):
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"""
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Process all experts in the Interaction Dataset
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For now, using NormalizedIntersimpleExpert with NRasterizedIncrementingAgent environment
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Args:
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filename (str): name for track file
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env_args (dict): default environment kwargs
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policy_args (dict): default policy kwargs
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"""
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I, J = len(intersim.LOCATIONS), intersim.MAX_TRACKS
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pbar = tqdm(total=I*J)
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for loc in range(I):
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for track in range(J):
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it_env_args = copy.deepcopy(env_args)
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it_env_args.update({
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'loc':loc,
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'track':track,
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})
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out_folder = os.path.join(intersim.LOCATIONS[loc], 'track%04i'%(track))
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if not os.path.isdir(out_folder):
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os.makedirs(out_folder)
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it_path = os.path.join(out_folder,filename)
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expert.demonstrations(
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expert='NormalizedIntersimpleExpert',
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env='NRasterizedIncrementingAgent',
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path=it_path,
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env_args=it_env_args,
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policy_args=policy_args,
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)
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pbar.update(1)
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pbar.close()
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if __name__=='__main__':
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import fire
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fire.Fire(process_all_experts)
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