def part_iii_evaluation(sim_filename):
print sim_filename
mdp = MDP("blank_2_actions_81_states_mdp.txt")
results = []
# prior: assume each transition seen once
transition_count = [[[0.1 for _ in range(81)] for _ in range(81)] for _ in range(2)]
for n in range(10):
print "Big loop " + str(n)
results.append([])
for i in range(100):
mdp, transition_count = adp_rl(mdp, Sim(MDP(sim_filename)), transition_count)
value_fn, policy, iterations = plan(mdp, 0.99, 0.01)
print "Value: " + str(value_fn)
print "Policy: " + str(policy)
# print "Reward: " + str(mdp.rewards)
# print "Transitions: " + str(mdp.transitions)
for i in range(100):
reward = run_policy(Sim(MDP(sim_filename)), policy)
results[n].append(reward)
print "Average reward of policy: " + str(average(results[n]))
for l in results:
print average(l)
def adp_rl(mdp, sim, transition_count):
p_explore = 0.1
while sim.time < 200:
old_state = sim.current
val, policy, iterations = plan(mdp, 0.95, 0.05)
# take action according to explore/exploit: epsilon-greedy
# if epsilon
# random
# else
# greedy
if random.random() < p_explore:
# choose random action
action = random.choice(range(sim.get_actions()))
# print "Random action: " + str(action)
else:
# choose policy action
action = policy[old_state]
reward = sim.do_action(action)
new_state = sim.current
# update model:
# reward[current] = reward we received on the action
# transition[action_taken][old_state][new_state] += epsilon
# somehow remove epsilon from all other transitions like transition[action_taken][old_state][s]
mdp.rewards[new_state] = reward
transition_count[action][old_state][new_state] += 1
mdp = update_transitions(mdp, transition_count)
return mdp, transition_count
def adp_rl(mdp, sim, transition_count):
p_explore = 0.1
while sim.time < 200:
old_state = sim.current
val, policy, iterations = plan(mdp, 0.95, 0.05)
# take action according to explore/exploit: epsilon-greedy
# if epsilon
# random
# else
# greedy
if random.random() < p_explore:
# choose random action
action = random.choice(range(sim.get_actions()))
#print "Random action: " + str(action)
else:
# choose policy action
action = policy[old_state]
reward = sim.do_action(action)
new_state = sim.current
# update model:
# reward[current] = reward we received on the action
# transition[action_taken][old_state][new_state] += epsilon
# somehow remove epsilon from all other transitions like transition[action_taken][old_state][s]
mdp.rewards[new_state] = reward
transition_count[action][old_state][new_state] += 1
mdp = update_transitions(mdp, transition_count)
return mdp, transition_count
def part_iii_evaluation(sim_filename):
print sim_filename
mdp = MDP("blank_2_actions_81_states_mdp.txt")
results = []
# prior: assume each transition seen once
transition_count = [[[0.1 for _ in range(81)] for _ in range(81)]
for _ in range(2)]
for n in range(10):
print "Big loop " + str(n)
results.append([])
for i in range(100):
mdp, transition_count = adp_rl(mdp, Sim(MDP(sim_filename)),
transition_count)
value_fn, policy, iterations = plan(mdp, 0.99, 0.01)
print "Value: " + str(value_fn)
print "Policy: " + str(policy)
#print "Reward: " + str(mdp.rewards)
#print "Transitions: " + str(mdp.transitions)
for i in range(100):
reward = run_policy(Sim(MDP(sim_filename)), policy)
results[n].append(reward)
print "Average reward of policy: " + str(average(results[n]))
for l in results:
print average(l)
def updatePlanQueue(self, TMR): if TMR: new_plan = self.planSelect(TMR) if new_plan == None: return heappush(self.plans, new_plan) if (len(new_plan.prerequisites) > 0): for prereq in new_plan.prerequisites: if prereq[0] == "knowledge": prereq[2] = self.kblookup(prereq[1]) if prereq[0] == "plan": pt = PlanList.plan_map[prereq[1]] prereq_plan = plan(new_plan.priority - 1,pt[1],0,True,prereq[1]) heappush(self.plans, prereq_plan) self.plans[0].executeOneTimestep(TMR) elif len(self.plans) > 0: self.plans[0].executeOneTimestep(TMR) else: return if self.plans[0].finished: heappop(self.plans)
def updatePlanQueue(self, TMR):
# Nothing to do in this case
if TMR == 0 and len(self.plans) == 0:
return
# Continue with current plan, guaranteed to be one if the previous condition wasn't met
elif TMR == 0:
self.plans[0].executeOneTimestep(TMR)
# New plan, and other plans exist already
else:
new_plan = self.planSelect(TMR)
if new_plan == None:
return
heappush(self.plans, new_plan)
if len(new_plan.prerequisites) > 0:
for prereq in new_plan.prerequisites:
if prereq[0] == "knowledge":
prereq[2] = self.kblookup(prereq[1])
if prereq[0] == "plan":
pt = PlanList.plan_map[prereq[1]]
prereq_plan = plan(new_plan.priority - 1, pt[1], 0, True, prereq[1])
heappush(self.plans, prereq_plan)
self.plans[0].executeOneTimestep(TMR)
if self.plans[0].finished:
heappop(self.plans)
def main() -> None:
mode_help = "Mode help."
# Get the description from the docstring of the function
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, usage=USAGE)
# Set values of default arguments
parser.set_defaults(dump="",
staging=str(data / "staging"),
server=str(data / "server"),
ignore=['.jsonl', '.json', '.aae'],
replace=False,
mode="copy")
parser.add_argument('-d',
'--dump',
type=str,
required=False,
help=mode_help)
parser.add_argument('--staging', type=str, required=False, help=mode_help)
parser.add_argument('--server', type=str, required=False, help=mode_help)
parser.add_argument('-i',
'--ignore',
type=list,
required=False,
help=mode_help)
parser.add_argument('-r',
'--replace',
type=bool,
required=False,
help=mode_help)
parser.add_argument('-m',
'--mode',
type=str,
required=False,
help=mode_help)
# Parse parameters
cli_args = parser.parse_args()
# Instantiate default args object
args = Arguments(cli_args.dump, staging_paths(cli_args.staging),
server_paths(cli_args.server), cli_args.ignore,
cli_args.replace, cli_args.mode)
print(args)
# Get user input
stage_opts = ["y", "n"]
stage_question = (
f"\nDo you want to stage files? {'/'.join(stage_opts)}: ")
stage_answer = cli_ask_question(question=stage_question,
options=stage_opts)
if stage_answer == "y":
validate_staging(args)
# Prepare migration from dump
plan_staging = plan(source=args.dump,
destinations=args.staging,
ignore=args.ignore)
# Execute migration
execute(df=plan_staging, mode=args.mode, replace=args.replace)
# Prepare migration to server from staging
plan_server = plan(source=args.staging["HOME"],
destinations=args.server,
ignore=args.ignore)
else:
# Prepare direct migration to server
plan_server = plan(source=args.dump,
destinations=args.server,
ignore=args.ignore)
# Confirm load job
load_options = ["y", "n"]
load_question = (
f"\nReady to load data to the server? {'/'.join(load_options)}: ")
load_answer = cli_ask_question(question=load_question,
options=load_options)
if load_answer == "y":
# Execute migration
execute(df=plan_server, mode=args.mode, replace=args.replace)
else:
print(f"\nAll files are ready to load in staging. Abortng.")
return
if stage_answer == "y":
# Cleanup
optionally_clean_dir(args.staging["HOME"])
#List of all rides
rides.append((coordenates, earliest, latest))
return params, rides
if __name__ == "__main__":
"""
uso:
python3 main.py nombre
"""
filename = sys.argv[1]
data, ridesRaw = get_info('input/{}.in'.format(filename))
rows, columns, nCars, nRides, bonus, TIME = data
cars = [Car() for _ in range(nCars)]
rides = [Ride(elem) for elem in ridesRaw]
maxDistance = rows + columns
results = plan(rides, cars, bonus, TIME, maxDistance)
with open("{}.out".format(filename), "w") as f:
for l in results:
l = list(map(str, l))
line = str(len(l)) + ' ' + ' '.join(l) + '\n'
f.write(line)
