def run_single_run(iw_parameters,iw_variants, run_num, Domain, sim_dt, sim_budget, horizon, numberRollouts, runNum, seed) :
import time
import numpy as np
import random
import json
import matplotlib.pyplot as plt
import gc
import wizluk
import pandas as pd
import sys
import gym
import wizluk.envs
import wizluk.policies
from wizluk.policies import RandomPolicy
import cv2
import warnings
def warn_with_traceback(message, category, filename, lineno, file=None, line=None):
log = file if hasattr(file,'write') else sys.stderr
traceback.print_stack(file=log)
log.write(warnings.formatwarning(message, category, filename, lineno, line))
warnings.showwarning = warn_with_traceback
wizluk.setup_logger("iw_gridworld_v1.log")
env = gym.make(Domain)
np.random.seed(seed)
env.seed(seed)
random.seed(seed)
IW_Rollout = wizluk.policies.UCT(**(iw_parameters[run_num]))
IW_Rollout_agent = wizluk.agents.LookaheadAgent(env, IW_Rollout, name='IW_Rollout', domain='GridWorld-16x16-v1')
IW_Rollout_df = {}
IW_Rollout_agent.init_evaluation_statistics(IW_Rollout_df)
S = np.prod(env.observation_space.shape)
x = env.reset()
x0 = copy.deepcopy(x)
IW_Rollout_agent.start_episode()
score = 0.0
for s in range(horizon):
wizluk.logger.debug("action number: {}".format(s))
x_flat = np.reshape(x, [1, S])
u = IW_Rollout_agent.get_action(x)
x_next, reward, done, info = env.step(u)
x_next_flat = np.reshape(x_next, [1, S])
IW_Rollout_agent.observe_transition(x_flat,u, reward, x_next_flat, done, False)
x = x_next
score += reward
if done:
break
IW_Rollout_agent.stop_episode()
IW_Rollout_agent.collect_evaluation_statistics( IW_Rollout_df, x0 )
with open('../results/{}_{}_simdt_{}_simBud_{}_Horizon_{}_numRoll_{}_runNum_{}.dat'.format(iw_variants['Name'][run_num],Domain, sim_dt, sim_budget, horizon, numberRollouts, runNum), 'wb') as output:
pickle.dump(score, output, pickle.HIGHEST_PROTOCOL)
def run_experiment(Domain, sim_dt, sim_budget, horizon, numberRollouts, N, seeds, seed, includeHeur, includeKuth) :
import time
import numpy as np
import random
import json
import matplotlib.pyplot as plt
import gc
import wizluk
import pandas as pd
import sys
import gym
import wizluk.envs
import wizluk.policies
from wizluk.policies import RandomPolicy
import cv2
import warnings
def warn_with_traceback(message, category, filename, lineno, file=None, line=None):
log = file if hasattr(file,'write') else sys.stderr
traceback.print_stack(file=log)
log.write(warnings.formatwarning(message, category, filename, lineno, line))
warnings.showwarning = warn_with_traceback
wizluk.setup_logger("iw_gridworld_v1.log")
iw_depth_random_rollout_parameters = {
"budget" : sim_dt,
"sim_budget" : sim_budget,
"horizon": horizon,
"cost_to_go_est": "random_rollout",
"num_rollouts": numberRollouts,
"atari": "True",
"caching": "Partial"
}
iw_depth_knuth_parameters = {
"budget" : sim_dt,
"sim_budget" : sim_budget,
"horizon": horizon,
"cost_to_go_est": "knuth",
"num_rollouts": numberRollouts,
"atari": "True",
"caching": "Partial"
}
iw_depth_heur_parameters = {
"budget" : sim_dt,
"sim_budget" : sim_budget,
"horizon": horizon,
"cost_to_go_est": "heuristic",
"atari": "True",
"caching": "Partial"
}
#iw_depth_stochastic_enum_parameters = {
#"budget" : sim_dt,
#"sim_budget" : sim_budget,
#"novelty_definition" : "depth",
#"include_root_in_novelty_table": "True",
#"horizon": horizon,
#"pruned_state_strategy": "stochastic_enum",
#"number_of_paths_to_consider_for_stoch_enum": 2,
#"num_rollouts": numberRollouts
#}
iw_variants = {'Name': []}#, "iw_depth_knuth"]}#, "iw_depth_heur"]}#, "iw_depth_stochastic_enum"]}
iw_parameters = [] #, iw_depth_knuth_parameters]#, iw_depth_heur_parameters]#, iw_depth_stochastic_enum_parameters]
if includeKuth:
iw_variants['Name'].append("OneStep_knuth")
iw_parameters.append(iw_depth_knuth_parameters)
if includeHeur:
iw_variants['Name'].append("OneStep_heur")
iw_parameters.append(iw_depth_heur_parameters)
listOfRuns = []
random.seed(seeds[seed])
for run_num in range(len(iw_parameters)) :
seedStart = random.randint(0,50000)
seedSkip = random.randint(1,201)
for k in range(N) :
listOfRuns.append(run_single_run.remote(iw_parameters, iw_variants, run_num, Domain, sim_dt, sim_budget, horizon, numberRollouts, k, seeds[seedStart + k * seedSkip]))
ray.get(listOfRuns)
gc.collect()
return 0