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Load policy config in evaluation

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ebuehrle
2022-02-28 12:23:02 +01:00
parent 62c28d0cfa
commit a37995694d
3 changed files with 33 additions and 9 deletions
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21
eval_experiments.py
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@@ -1,17 +1,23 @@
import os
from src.eval_main import eval_main
from src.evaluation.utils import load_and_average
import torch
import json
activations = [torch.nn.Tanh, torch.nn.LeakyReLU]
def main(method:str='expert', folder:str=None, locations=[(0,0)], skip_running=False):
exclude_keys_from_policy_kwargs = {'learning_rate', 'learning_rate_decay', 'clip_ratio', 'iterations_per_epoch', 'option'}
policy_kwargs = {}
if method in ['expert', 'idm']:
env, env_kwargs ='NRasterizedRouteIncrementingAgent', {}
elif method in ['bc','gail']:
env='NormalizedContinuousEvalEnv'
env_kwargs={'stop_on_collision':True, 'max_episode_steps':1000}
elif method in ['hail']:
env = 'NormalizedOptionsEvalEnv'
env = 'NormalizedSafeOptionsEvalEnv'
env_kwargs={'stop_on_collision':True, 'max_episode_steps':1000, 'safe_actions_collision_method': None, 'abort_unsafe_collision_method': None}
elif method in ['shail']:
env = 'NormalizedSafeOptionsEvalEnv'
@@ -23,7 +29,18 @@ def main(method:str='expert', folder:str=None, locations=[(0,0)], skip_running=F
if folder is not None:
files = [os.path.join(folder, f) for f in os.listdir(folder) if os.path.isfile(os.path.join(folder, f))]
print('%i folders found in %s folder' %(len(files), folder))
files = [f for f in files if f.endswith('.pt')]
with open(os.path.join(folder, 'config.json'), 'rb') as f:
config = json.load(f)
print('%i policy files found in %s folder' %(len(files), folder))
print('found policy config', config['policy'])
policy_config = {k: v for k, v in config['policy'].items() if k not in exclude_keys_from_policy_kwargs}
policy_config['activation'] = activations[policy_config['activation']]
print('final policy config', policy_config)
policy_kwargs.update(policy_config)
print('final policy kwargs', policy_kwargs)
if not skip_running:
for policy_file in files: