###A script to test behavior cloning
#Config for FLUIDS simulator
with open('configs/fluids_config.json') as json_data_file:
fluids_config = json.load(json_data_file)
#Config for Imitation Learning Experiment
with open('configs/il_velocity_config.json') as json_data_file:
il_config = json.load(json_data_file)
file_path = il_config['file_path'] + il_config['experiment_name']
il_config['model'] = DecisionTreeClassifier(max_depth=4)
#Trainer class
trainer = Trainer(fluids_config, il_config)
#Plotter class
plotter = Plotter(file_path)
stats = []
for i in range(il_config['num_iters']):
#Collect demonstrations
trainer.collect_supervisor_rollouts()
#update model
trainer.train_model()
#Evaluate Policy
stats.append(trainer.get_stats())
#Save plots
plotter.save_plots(stats)
###A script to test behavior cloning
#Config for FLUIDS simulator
with open('configs/fluids_config.json') as json_data_file:
fluids_config = json.load(json_data_file)
#Config for Imitation Learning Experiment
with open('configs/il_covariate_config.json') as json_data_file:
il_config = json.load(json_data_file)
fluids_config['environment']['visualize'] = False
###### SELECT PARAMETER #################
il_config['time_horizon'] = 5
il_config['num_sup_rollouts'] = 4
il_config['experiment_name'] = il_config[
'trial_name'] + 'save_unit_test_' + str(rand.uniform())
trainer = Trainer(fluids_config, il_config)
trainer.collect_supervisor_rollouts()
trainer.train_model()
train_sup = trainer.il_learn.get_train_error()
loss_sup, _ = trainer.il_learn.get_test_error()
assert (train_sup >= 0.0)
assert (loss_sup >= 0.0)
import json
import glob
from il_fluids.core import Trainer
import os.path
###A script to test behavior cloning
#Config for FLUIDS simulator
with open('configs/fluids_config.json') as json_data_file:
fluids_config = json.load(json_data_file)
#Config for Imitation Learning Experiment
with open('configs/il_covariate_config.json') as json_data_file:
il_config = json.load(json_data_file)
fluids_config['environment']['visualize'] = True
###### SELECT PARAMETER #################
il_config['time_horizon'] = 5
il_config['num_sup_rollouts'] = 4
il_config['num_iters'] = 1
il_config['experiment_name'] = il_config[
'trial_name'] + 'save_unit_test_' + str(rand.uniform())
file_path = il_config['file_path'] + il_config['experiment_name']
trainer = Trainer(fluids_config, il_config)
trainer.train_robot()
assert (os.path.isfile(file_path + '/plots/reward.png'))
import os.path
###A script to test behavior cloning
#Config for FLUIDS simulator
with open('configs/fluids_config.json') as json_data_file:
fluids_config = json.load(json_data_file)
#Config for Imitation Learning Experiment
with open('configs/il_covariate_config.json') as json_data_file:
il_config = json.load(json_data_file)
fluids_config['environment']['visualize'] = False
###### SELECT PARAMETER #################
il_config['time_horizon'] = 5
il_config['num_sup_rollouts'] = 4
il_config['num_iters'] = 1
il_config['experiment_name'] = il_config[
'trial_name'] + 'save_unit_test_' + str(rand.uniform())
file_path = il_config['file_path'] + il_config['experiment_name']
trainer = Trainer(fluids_config, il_config)
trainer.set_tracker(BaseTracker)
trainer.train_robot()
assert (os.path.isfile(file_path + '/tracker/initial_state.npy'))
import cProfile
import time
import numpy as np
import numpy.linalg as LA
import json
from il_fluids.core import Trainer
from sklearn.tree import DecisionTreeClassifier
from il_fluids.data_protocols import *
###A script to test behavior cloning
#Config for FLUIDS simulator
with open('configs/fluids_config.json') as json_data_file:
fluids_config = json.load(json_data_file)
#Config for Imitation Learning Experiment
with open('configs/il_covariate_config.json') as json_data_file:
il_config = json.load(json_data_file)
fluids_config['environment']['visualize'] = False
il_config['time_horizon'] = 5
###### SELECT MODEL #################
trainer = Trainer(fluids_config, il_config)
rollout = trainer.rollout_supervisor()
assert (len(rollout) == 5)
with open('configs/il_covariate_two_config.json') as json_data_file:
il_config = json.load(json_data_file)
###### SELECT MODEL #################
il_config['model'] = DecisionTreeClassifier(max_depth=10)
fluids_config['environment']['visualize'] = True
# ###RUN BEHAVIOR CLONING############
# il_config['experiment_name'] = il_config['trial_name'] + "_30_noise_injection"
# trainer = Trainer(fluids_config,il_config)
# dcp = DART()
# dcp.noise = 0.3
# trainer.set_data_protocol(dcp)
# trainer.train_robot()
il_config['experiment_name'] = il_config['trial_name'] + "_10_noise_neural"
trainer = Trainer(fluids_config, il_config)
trainer.set_data_protocol(DART())
trainer.train_robot()
# il_config['experiment_name'] = il_config['trial_name'] + "_bc"
# trainer = Trainer(fluids_config,il_config)
# trainer.train_robot()
import gym
import gym_urbandriving as uds
import cProfile
import time
import numpy as np
import numpy.linalg as LA
import json
from il_fluids.core import Trainer
from il_fluids.core import Learner
from il_fluids.core import Plotter
###A script to test behavior cloning
#Config for FLUIDS simulator
with open('configs/fluids_config.json') as json_data_file:
fluids_config = json.load(json_data_file)
#Config for Imitation Learning Experiment
with open('configs/il_velocity_config.json') as json_data_file:
il_config = json.load(json_data_file)
file_path = il_config['file_path'] + il_config['experiment_name']
#Trainer class
trainer = Trainer(fluids_config, il_config)
trainer.train_model()
trainer.get_stats()
import json
import glob
from il_fluids.core import Trainer
###A script to test behavior cloning
#Config for FLUIDS simulator
with open('configs/fluids_config.json') as json_data_file:
fluids_config = json.load(json_data_file)
#Config for Imitation Learning Experiment
with open('configs/il_covariate_config.json') as json_data_file:
il_config = json.load(json_data_file)
fluids_config['environment']['visualize'] = False
###### SELECT PARAMETER #################
il_config['time_horizon'] = 5
il_config['num_sup_rollouts'] = 4
il_config['experiment_name'] = il_config[
'trial_name'] + 'save_unit_test_' + str(rand.uniform())
trainer = Trainer(fluids_config, il_config)
trainer.collect_supervisor_rollouts()
trainer.train_model()
loss_robot, reward, _ = trainer.evaluate_policy()
assert (loss_robot >= 0.0)