parser = argparse.ArgumentParser(description=None)
parser.add_argument('--env_name', help="name of the environment, e.g. 'breakout'")
parser.add_argument('--mcmc_file', help="name of mcmc file for chain")
parser.add_argument('--no_term', action = 'store_true')
parser.add_argument('--alpha', type=float, help="value of alpha-VaR, e.g. alpha = 0.05")
args = parser.parse_args()
print(args)
alpha = args.alpha
print(args.env_name)
#read in the weights as a 2-d array and the feature counts of the policy
W, log_lik = helper.get_weightchain_array(args.mcmc_file, return_likelihood=True)
print(np.mean(W, axis=0))
eval_policies = ['00025', '00325', '00800', '01450', 'mean', 'map']
name_transform = {'00025':'policy A', '00325':'policy B', '00800':'policy C', '01450':'policy D', 'mean':'mean', 'map':'MAP', 'noop': 'no-op'}
if args.env_name == "enduro":
eval_policies =['03125', '03425', '03900', '04875', 'mean', 'map']
name_transform = {'03125':'policy A', '03425':'policy B', '03900':'policy C', '04875':'policy D', 'mean':'mean', 'map':'MAP', 'noop': 'no-op'}
gt_return_list = []
fcount_list = []
return_dist_list = []
print(" policy & mean & " + str(alpha) + "-VaR & ave length & gt & min gt \\\\ \hline")
for eval in eval_policies:
#print("-"*20)
#print("eval", name_transform[eval])
returns, fcounts = helper.parse_avefcount_array('../../policies/' + args.env_name +'_' + eval + '_fcounts_onehot_truncated_terminal' + str(args.no_term) + '.txt')
#print(fcounts)
'axes.labelsize': 'x-large',
'axes.titlesize': 'x-large',
'xtick.labelsize': 'x-large',
'ytick.labelsize': 'x-large'
}
plt.rcParams.update(params)
#plt.style.use('seaborn-deep')
parser = argparse.ArgumentParser(description=None)
parser.add_argument('--env_name',
help="name of the environment, e.g. 'breakout'")
args = parser.parse_args()
#read in the weights as a 2-d array and the feature counts of the policy
W = helper.get_weightchain_array("../../mcmc_data/" + args.env_name + "_0.txt")
print(np.mean(W, axis=0))
eval_policies = ['00025', '00325', '00800', '01450']
if args.env_name == "enduro":
eval_policies = ['03125', '03425', '03900', '04875']
gt_return_list = []
fcount_list = []
return_dist_list = []
print(" policy & mean & 0.05-VaR & ave length & min & stdev")
for eval in eval_policies:
#print("-"*20)
#print("eval", eval)
returns, fcounts = helper.parse_avefcount_array('../../policies/' +
args.env_name + '_' +
eval + '_fcounts_100.txt')
return_dist = np.dot(W, fcounts)
parser.add_argument('--alpha',
type=float,
help="value of alpha-VaR, e.g. alpha = 0.05")
parser.add_argument('--identifier',
help="keyword to find correct fcount files")
parser.add_argument('--plot', action='store_true')
parser.add_argument('--noop', action='store_true')
args = parser.parse_args()
print("alpha:", args.alpha)
print(args.env_name)
#read in the weights as a 2-d array and the feature counts of the policy
W, likelihood = helper.get_weightchain_array(args.mcmc_file,
burn=5000,
skip=20,
return_likelihood=True,
preburn_until_accept=True)
print("make sure that I actually accepted a value")
print(np.sum(likelihood == -float('inf')))
W = W[likelihood != -float('inf')]
#print(np.mean(W, axis=0))
eval_policies = ['00025', '00325', '00800', '01450', 'mean', 'map', 'no-op']
name_transform = {
'00025': 'A',
'00325': 'B',
'00800': 'C',
'01450': 'D',
'mean': 'Mean',
'map': 'MAP',
import numpy as np
import helper
import argparse
parser = argparse.ArgumentParser(description=None)
parser.add_argument('--env_name', help="name of the environment, e.g. 'breakout'")
parser.add_argument('--eval_fcounts', help='file with policy fcounts'
parser.add_argument('--mcmc_file', help="name of mcmc file for chain")
parser.add_argument('--alpha', type=float, help="value of alpha-VaR, e.g. alpha = 0.05")
args = parser.parse_args()
print(args.env_name)
#read in the weights as a 2-d array and the feature counts of the policy
W = helper.get_weightchain_array(args.mcmc_file)
print(np.mean(W, axis=0))
print("mean & 0.05-VaR & gt & ave length")
fcounts, returns, lengths, all_fcounts = helper.parse_fcount_policy_eval(args.eval_fcounts)
return_dist = np.dot(W,fcounts)
print("{:.1f} & {:.1f} & {:.1f} & {:.1f} \\\\".format(np.mean(return_dist),helper.worst_percentile(return_dist, args.alpha), np.mean(returns), np.mean(lengths)))
import numpy as np
import helper
import argparse
#Create tables for NeurIPS workshop paper but with normalized feature counts to see if that helps!
#I'm going to try the L1 and L2 norms
parser = argparse.ArgumentParser(description=None)
parser.add_argument('--env_name',
help="name of the environment, e.g. 'breakout'")
args = parser.parse_args()
print(args.env_name)
#read in the weights as a 2-d array and the feature counts of the policy
W = helper.get_weightchain_array("../../mcmc_data/" + args.env_name +
"_0.txt") #these are all normalized
#print(np.mean(W, axis=0))
eval_policies = ['00025', '00325', '00800', '01450', 'mean', 'map']
name_transform = {
'00025': 'policy A',
'00325': 'policy B',
'00800': 'policy C',
'01450': 'policy D',
'mean': 'mean',
'map': 'MAP',
'noop': 'no-op'
}
if args.env_name == "enduro":
eval_policies = ['03125', '03425', '03900', '04875', 'mean', 'map']
name_transform = {
'03125': 'policy A',
import numpy as np
import helper
import argparse
parser = argparse.ArgumentParser(description=None)
parser.add_argument('--env_name', help="name of the environment, e.g. 'breakout'")
alpha = 0.01
args = parser.parse_args()
print(args.env_name)
#read in the weights as a 2-d array and the feature counts of the policy
W, log_lik = helper.get_weightchain_array("../../mcmc_data/" + args.env_name + "_onehot_chain.txt", return_likelihood=True)
print(np.mean(W, axis=0))
eval_policies = ['00025', '00325', '00800', '01450', 'mean', 'map']
name_transform = {'00025':'policy A', '00325':'policy B', '00800':'policy C', '01450':'policy D', 'mean':'mean', 'map':'MAP', 'noop': 'no-op'}
if args.env_name == "enduro":
eval_policies =['03125', '03425', '03900', '04875', 'mean', 'map']
name_transform = {'03125':'policy A', '03425':'policy B', '03900':'policy C', '04875':'policy D', 'mean':'mean', 'map':'MAP', 'noop': 'no-op'}
gt_return_list = []
fcount_list = []
return_dist_list = []
print(" policy & mean & " + str(alpha) + "-VaR & ave length & gt & min gt \\\\ \hline")
for eval in eval_policies:
#print("-"*20)
#print("eval", name_transform[eval])
returns, fcounts = helper.parse_avefcount_array('../../policies/' + args.env_name +'_' + eval + '_fcounts_onehot.txt')
#print(fcounts)
return_dist = np.dot(W,fcounts)
parser = argparse.ArgumentParser(description=None)
parser.add_argument('--env_name', help="name of the environment, e.g. 'breakout'")
parser.add_argument('--mcmc_file', help="name of mcmc file for chain")
parser.add_argument('--alpha', type=float, help="value of alpha-VaR, e.g. alpha = 0.05")
parser.add_argument('--plot', action='store_true')
parser.add_argument('--noop', action='store_true')
args = parser.parse_args()
print("alpha:", args.alpha)
print(args.env_name)
#read in the weights as a 2-d array and the feature counts of the policy
W = helper.get_weightchain_array(args.mcmc_file, burn=2000, skip=50)
#print(np.mean(W, axis=0))
eval_files = []
for file in os.listdir('/scratch/cluster/dsbrown/rl_policies'):
if args.env_name in file:
print(file)
eval_files.append(file)
for f in eval_files:
avefcounts, returns, lengths, fcounts = helper.parse_fcount_policy_eval('/scratch/cluster/dsbrown/rl_policies/' + f)
print(f)
print(np.mean(returns), np.mean(lengths))
#input()
alphas = np.linspace(0,1.0,21)