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removing gail-trpo since performance is about the same as gail, adding experiment evaluation script, updating metric averaging to work

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This commit is contained in:
Arec Jamgochian
2022-02-27 23:03:28 -08:00
parent e110570092
commit e37911eeff
5 changed files with 133 additions and 329 deletions
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31
src/eval_main.py
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@@ -15,7 +15,7 @@ from src.options import envs as options_envs2
from src.safe_options.policy import SetMaskedDiscretePolicy
from src.safe_options import options as options_envs3
from src.util.wrappers import IntersimpleTimeLimit
import os
from typing import Optional, List, Dict, Tuple
import torch
import numpy as np
@@ -44,29 +44,29 @@ def load_policy(method:str,
policy = SetPolicy(env.action_space.shape[-1])
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
elif method == 'gail':
elif method == 'gail-trpo':
policy = SetPolicy(env.action_space.shape[-1])
policy(torch.zeros(env.observation_space.shape))
policy = ReparamPolicy(policy)
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
elif method == 'gail-ppo':
elif method == 'gail':
policy = SetPolicy(env.action_space.shape[-1])
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
elif method == 'rail':
raise NotImplementedError
elif method == 'ogail':
elif method == 'hail-trpo':
policy = SetDiscretePolicy(env.action_space.n)
policy(torch.zeros(env.observation_space.shape))
policy = ReparamPolicy(policy)
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
elif method == 'ogail-ppo':
elif method == 'hail':
policy = SetDiscretePolicy(env.action_space.n)
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
elif method == 'sgail':
elif method == 'shail-trpo':
policy = SetMaskedDiscretePolicy(env.action_space.n)
policy(
torch.zeros(env.observation_space['observation'].shape),
@@ -75,7 +75,7 @@ def load_policy(method:str,
policy = ReparamSafePolicy(policy)
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
elif method == 'sgail-ppo':
elif method == 'shail':
policy = SetMaskedDiscretePolicy(env.action_space.n)
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
@@ -375,6 +375,9 @@ def eval_main(
policy_file (str): path to saved policy
env (str): environment class
method (str): method (expert, bc, gail, rail, hgail, hrail)
Returns:
outbase (str): string to outbase
"""
print(f'#############################################################################')
print(f'Evaluating {method} from file {policy_file} on {env} at locations {locations}')
@@ -383,9 +386,17 @@ def eval_main(
# set seed
np.random.seed(seed)
torch.manual_seed(seed)
pfilename = policy_file.split('/')[-1].split('.')[0]
locstr = 'loc_'+'_'.join([f'r{ro}t{tr}' for (ro,tr) in locations])
outbase = f'out/{method}/{locstr}/{pfilename}_seed{seed}'
if policy_file == '':
method_path = method
name_base = method
else:
path_items = policy_file.split('/')
name_base = path_items[-1].split('.')[0]
method_path = ('/').join(path_items[1:-1])
outfolder = os.path.join('out',method_path,locstr)
filebase = name_base + f'_tseed{seed}'
outbase = os.path.join(outfolder,filebase)
# load expert metrics
expert_metrics = generate_expert_metrics(locations)
@@ -404,6 +415,8 @@ def eval_main(
save_metrics(smetrics, outbase+'_summary.pkl')
cmetrics = comparison_metrics(policy_metrics, expert_metrics, outbase=outbase)
save_metrics(cmetrics, outbase+'_comparison.pkl')
return outbase
if __name__=='__main__':
import fire