def run(self, algo, T, **kwargs):
visualiser = GraphViz(description='tmp')
params, pomdp = self.params, None
total_rewards, budget = 0, params.budget
log.info('~~~ initialising ~~~')
with PomdpParser(params.env_config) as ctx:
# creates model and solver
model = self.create_model(ctx.copy_env())
pomdp = self.create_solver(algo, model)
# supply additional algo params
belief = ctx.random_beliefs(
) if params.random_prior else ctx.generate_beliefs()
if algo == 'pbvi':
belief_points = ctx.generate_belief_points(kwargs['stepsize'])
pomdp.add_configs(belief_points)
elif algo == 'pomcp':
pomdp.add_configs(budget, belief, **kwargs)
# have fun!
log.info('''
++++++++++++++++++++++
Starting State: {}
Starting Budget: {}
Time Horizon: {}
Max Play: {}
++++++++++++++++++++++'''.format(model.curr_state, budget, T,
params.max_play))
for i in range(params.max_play):
# plan, take action and receive environment feedbacks
pomdp.solve(T)
action = pomdp.get_action(belief)
new_state, obs, reward, cost = pomdp.take_action(action)
if params.snapshot and isinstance(pomdp, POMCP):
# takes snapshot of belief tree before it gets updated
self.snapshot_tree(visualiser, pomdp.tree, '{}.gv'.format(i))
# update states
belief = pomdp.update_belief(belief, action, obs)
total_rewards += reward
budget -= cost
# print ino
log.info('\n'.join([
'Taking action: {}'.format(action),
'Observation: {}'.format(obs),
'Reward: {}'.format(reward),
'Budget: {}'.format(budget),
'New state: {}'.format(new_state),
## 'New Belief: {}'.format(belief),
'=' * 20
]))
if budget <= 0:
log.info('Budget spent.')
if action == 'Catch' and ('tagged' in new_state):
break
input("Pulsa intro para ejecutar el siguiente paso del algoritmo")
log.info('{} games played. Total reward = {}'.format(
i + 1, total_rewards))
return pomdp
def run(self, modo, problema, algo, T, **kwargs):
steps = math.array([], float)
rewards = math.array([], float)
if modo == "Benchmark":
c = 0
else:
c = 29
while c < 30:
c += 1
if modo == "Benchmark":
log.info("===================== Ejecucion " + str(c) +
'=====================')
visualiser = GraphViz(description='tmp')
params, pomdp = self.params, None
total_rewards, budget = 0, params.budget
with PomdpParser(params.env_config) as ctx:
model = self.create_model(ctx.copy_env())
pomdp = self.create_solver(algo, model)
belief = ctx.random_beliefs(
) if params.random_prior else ctx.generate_beliefs()
if algo == 'pbvi':
belief_points = ctx.generate_belief_points(
kwargs['stepsize'])
pomdp.add_configs(belief_points)
elif algo == 'pomcp':
pomdp.add_configs(budget, belief, **kwargs)
if modo != "Benchmark":
log.info('''
++++++++++++++++++++++
Estado inicial: {}
Presupuesto: {}
Creencia: {}
Horizonte de tiempo: {}
Numero de juegos maximo: {}
++++++++++++++++++++++'''.format(model.curr_state, budget,
belief, T, params.max_play))
condicion_parada = False
i = 0
while not condicion_parada and params.max_play > i:
i += 1
pomdp.solve(T, modo)
action = pomdp.get_action(belief)
new_state, obs, reward, cost = pomdp.take_action(action)
if problema == "Tigre":
condicion_parada = action == "open-left" or action == "open-right"
elif problema == "LaserTag":
condicion_parada = action == "Catch"
elif problema == "Recipientes":
condicion_parada = action == "bebe-izq" or action == "bebe-med" or action == "bebe-der"
if params.snapshot and isinstance(pomdp, POMCP):
self.snapshot_tree(visualiser, pomdp.tree,
'{}.gv'.format(i))
belief = pomdp.update_belief(belief, action, obs)
total_rewards += reward
budget -= cost
if modo == "Interactivo":
log.info('\n'.join([
'Accion tomada: {}'.format(action),
'Observacion: {}'.format(obs),
'Recompensa: {}'.format(reward),
'Presupuesto: {}'.format(budget),
'Nuevo estado: {}'.format(new_state),
'Nueva creencia: {}'.format(belief),
'Paso numero: {}'.format(i), '=' * 20
]))
if budget <= 0:
log.info('Se ha sobrepasado el presupuesto establecido.')
if params.max_play != 'inf' and params.max_play <= i:
log.info(
'Se ha sobrepasado el número máximo de pasos establecido.'
)
log.info('{} pasos ejecutados. Recompensa total acumulada = {}\n'.
format(i, total_rewards))
steps = math.append(steps, i)
rewards = math.append(rewards, total_rewards)
if modo == "Benchmark":
mean_steps = steps.mean()
std_steps = steps.std()
mean_rewards = rewards.mean()
std_rewards = rewards.std()
print(
"#########################################################################################"
)
print("# RESULTADOS DEL BENCHMARK:")
print("# Valor medio pasos: ", mean_steps)
print("# Desviacion tipica pasos: ", std_steps)
print("# Valor medio recompensas: ", mean_rewards)
print("# Desviacion tipica recompensas: ", std_rewards)
print(
"#########################################################################################"
)
return pomdp
def replay(self, algo, T, **kwargs):
visualiser = GraphViz(description='tmp')
params, pomdp = self.params, None
total_rewards, budget = 0, params.budget
log.info('~~~ initialising experience replay ~~~')
## 4 experiences
with PomdpParser(params.env_config) as ctx:
for simulation in range(4):
log.info('~~~ initialising simulation: ' + str(simulation) +
'~~~')
# creates model and solver
model = self.create_model(ctx.copy_env())
pomdp = self.create_solver(algo, model)
# supply additional algo params
belief = ctx.random_beliefs(
) if params.random_prior else ctx.generate_beliefs()
if algo == 'pbvi':
# charging alphavec policy file
# belief_points = pomdp.generate_reachable_belief_points(belief, 50)
# pomdp.add_configs(belief_points)
pomdp.charging_policy(params.policyfile)
# pomdp.solve(T)
elif algo == 'pomcp':
pomdp.add_configs(budget, belief, **kwargs)
total_rewards = 0
# have fun!
log.info('''
++++++++++++++++++++++
Init Belief: {}
Max Play: {}
++++++++++++++++++++++'''.format(belief, params.max_play))
for i in range(params.max_play):
# plan, take action and receive environment feedbacks
if algo == 'pomcp':
pomdp.solve(T)
# take action
action = pomdp.get_action(belief)
# new_state, obs, reward, cost = pomdp.take_action(action)
# getting exp action
exp_action = self.getting_mode_from_expfile(
i, simulation, pomdp)
if exp_action == -1:
log.info('\n'.join(
['Observation: {}'.format(obs), 'Mission ended']))
plotting.destroy()
break
if params.snapshot and isinstance(pomdp, POMCP):
# takes snapshot of belief tree before it gets updated
self.snapshot_tree(visualiser, pomdp.tree,
'{}.gv'.format(i))
if i == 0:
plotting = AnimateBeliefPlot(belief, action,
exp_action)
else:
plotting.update(belief, action, exp_action, obs)
# getting features to play symbolic observation
features = self.getting_features_from_expfile(
i, simulation, pomdp)
#print(features)
label = self.classif_model.predict(features)
#print(label)
# transforming label in pomdp observation
available_observations = pomdp.model.observations
obs = available_observations[int(label[0])]
belief = pomdp.update_belief(belief, exp_action, obs)
# print loginfo
log.info('\n'.join([
'Observation: {}'.format(obs),
'POMDP would take action: {}'.format(action),
'action taken during EXPERIMENT: {}'.format(
exp_action), 'New Belief: {}'.format(belief),
'=' * 20
]))
return pomdp
def run(self, algo, T, **kwargs):
visualiser = GraphViz(description='tmp')
params, pomdp = self.params, None
total_rewards, budget = 0, params.budget
environment = params.env_config
benchmark = params.benchmark
log.info('~~~ initialising ~~~')
with PomdpParser(params.env_config) as ctx:
# creates model and solver
model = self.create_model(ctx.copy_env())
pomdp = self.create_solver(algo, model)
# supply additional algo params
belief = ctx.random_beliefs(
) if params.random_prior else ctx.generate_beliefs()
if algo == 'pbvi':
belief_points = ctx.generate_belief_points(kwargs['stepsize'])
pomdp.add_configs(belief_points)
elif algo == 'pomcp':
pomdp.add_configs(budget, belief, **kwargs)
# have fun!
log.info('''
++++++++++++++++++++++
Starting State: {}
Starting Budget: {}
Init Belief: {}
Time Horizon: {}
Max Play: {}
++++++++++++++++++++++'''.format(model.curr_state, budget, belief, T,
params.max_play))
for i in range(params.max_play):
# plan, take action and receive environment feedbacks
pomdp.solve(T)
action = pomdp.get_action(belief)
new_state, obs, reward, cost = pomdp.take_action(action)
if params.snapshot and isinstance(pomdp, POMCP):
# takes snapshot of belief tree before it gets updated
self.snapshot_tree(visualiser, pomdp.tree, '{}.gv'.format(i))
# update states
belief = pomdp.update_belief(belief, action, obs)
total_rewards += reward
budget -= cost
# Printing the details for every step of the interactive simulation
# log.info('\n'.join([
# 'Taking action: {}'.format(action),
# 'Observation: {}'.format(obs),
# 'Reward: {}'.format(reward),
# 'Budget: {}'.format(budget),
# 'New state: {}'.format(new_state),
# 'New Belief: {}'.format(belief),
# '=' * 20
# ]))
# Tiger problem ----------------------------------------------------------------
# When the open action is selected, the tiger problem will end, either the person scapes or is eaten by the tiger, so it has to stop.
if "Tiger-2D.POMDP" in environment:
if "open" in action:
log.info('\n'.join([
'Taking action: {}'.format(action),
'Observation: {}'.format(obs),
'Reward: {}'.format(reward), '=' * 20
]))
break
log.info('\n'.join([
'Taking action: {}'.format(action),
'Observation: {}'.format(obs), 'Reward: {}'.format(reward),
'New Belief: {}'.format(belief), '=' * 20
]))
# Web ads problem ----------------------------------------------------------------
# When the adv action is selected, the web ad problem will end, either the person gets a tie or a skate advertisement so it has to stop.
if "Web.POMDP" in environment:
if params.benchmark == 0:
if "adv" in action:
log.info('\n'.join([
'Taking action: {}'.format(action),
'Observation: {}'.format(obs),
'Reward: {}'.format(reward), '=' * 20
]))
break
log.info('\n'.join([
'Taking action: {}'.format(action),
'Observation: {}'.format(obs),
'Reward: {}'.format(reward),
'New Belief: {}'.format(belief), '=' * 20
]))
# Landing problem ----------------------------------------------------------------
# When the arrive action is selected, the landing problem will end, either the tripulation finds a treasure or they die horribly to the creatures in the landing.
if "Landing.POMDP" in environment:
if "arrive" in action:
log.info('\n'.join([
'Taking action: {}'.format(action),
'Observation: {}'.format(obs),
'Reward: {}'.format(reward), '=' * 20
]))
break
log.info('\n'.join([
'Taking action: {}'.format(action),
'Observation: {}'.format(obs), 'Reward: {}'.format(reward),
'New Belief: {}'.format(belief), '=' * 20
]))
#Tag problem ----------------------------------------------------------------
# When the status is tagger, the robot s will catch robot t, the tag problem will end so it has to stop.
if "Tag.POMDP" in environment:
if params.benchmark == 0:
if "tagged" in model.curr_state:
log.info('\n'.join([
'Taking action: {}'.format(action),
'Observation: {}'.format(obs),
'Reward: {}'.format(reward), '=' * 20
]))
break
log.info('\n'.join([
'Taking action: {}'.format(action),
'Observation: {}'.format(obs),
'Reward: {}'.format(reward),
'New state: {}'.format(new_state),
#'New Belief: {}'.format(belief),
'=' * 20
]))
# Printing the total steps and reward when the loop ends.
if params.benchmark == 0:
log.info(
'Simulation ended after {} steps. Total reward = {}'.format(
i + 1, total_rewards))
return pomdp
def runBench(self, algo, T, **kwargs):
visualiser = GraphViz(description='tmp')
params, pomdp = self.params, None
total_rewards, budget = 0, params.budget
environment = params.env_config
benchmark = params.benchmark
log.info('~~~ Initialising simulation ~~~')
with PomdpParser(params.env_config) as ctx:
# creates model and solver
model = self.create_model(ctx.copy_env())
pomdp = self.create_solver(algo, model)
# supply additional algo params
belief = ctx.random_beliefs(
) if params.random_prior else ctx.generate_beliefs()
if algo == 'pbvi':
belief_points = ctx.generate_belief_points(kwargs['stepsize'])
pomdp.add_configs(belief_points)
elif algo == 'pomcp':
pomdp.add_configs(budget, belief, **kwargs)
# have fun!
log.info('''
++++++++++++++++++++++
Starting State: {}
Starting Budget: {}
Init Belief: {}
Time Horizon: {}
Max Play: {}
++++++++++++++++++++++'''.format(model.curr_state, budget, belief,
T, params.max_play))
for i in range(params.max_play):
# plan, take action and receive environment feedbacks
pomdp.solve(T)
action = pomdp.get_action(belief)
new_state, obs, reward, cost = pomdp.take_action(action)
if params.snapshot and isinstance(pomdp, POMCP):
# takes snapshot of belief tree before it gets updated
self.snapshot_tree(visualiser, pomdp.tree, '{}.gv'.format(i))
# update states
belief = pomdp.update_belief(belief, action, obs)
total_rewards += reward
budget -= cost
#Computing final results when a problem stops
if "open" in action or "tagged" in model.curr_state or "adv" in action or "arrive" in action:
log.info('Ended simulation after {} steps. Total reward = {}'.
format(i + 1, total_rewards))
self.step_list.append(i + 1)
self.fReward_list.append(total_rewards)
self.steps += i + 1
self.fReward += total_rewards
break
# Printing the details for every step of the interactive simulation
# log.info('\n'.join([
# 'Taking action: {}'.format(action),
# 'Observation: {}'.format(obs),
# 'Reward: {}'.format(reward),
# 'Budget: {}'.format(budget),
# 'New state: {}'.format(new_state),
# 'New Belief: {}'.format(belief),
# '=' * 20
# ]))
if budget <= 0:
log.info('Budget spent.')
return pomdp
def replay(self, algo, T, **kwargs):
visualiser = GraphViz(description='tmp')
params, pomdp = self.params, None
total_rewards, budget = 0, params.budget
log.info('~~~ initialising experience replay ~~~')
with PomdpParser(params.env_config) as ctx:
total_rewards_simulations = []
for simulation in range(params.sim):
log.info('~~~ initialising simulation: ' + str(simulation) +
'~~~')
# creates model and solver
model = self.create_model(ctx.copy_env())
pomdp = self.create_solver(algo, model)
# supply additional algo params
belief = ctx.random_beliefs(
) if params.random_prior else ctx.generate_beliefs()
if algo == 'pbvi':
# charging alphavec policy file
# belief_points = pomdp.generate_reachable_belief_points(belief, 50)
# pomdp.add_configs(belief_points)
pomdp.charging_policy(params.policyfile)
# pomdp.solve(T)
elif algo == 'pomcp':
pomdp.add_configs(budget, belief, **kwargs)
total_rewards = 0
# have fun!
log.info('''
++++++++++++++++++++++
Starting State: {}
Init Belief: {}
Max Play: {}
++++++++++++++++++++++'''.format(model.curr_state, belief,
params.max_play))
for i in range(params.max_play):
# plan, take action and receive environment feedbacks
if algo == 'pomcp':
pomdp.solve(T)
action = pomdp.get_action(belief)
new_state, obs, reward, cost = pomdp.take_action(action)
if params.snapshot and isinstance(pomdp, POMCP):
# takes snapshot of belief tree before it gets updated
self.snapshot_tree(visualiser, pomdp.tree,
'{}.gv'.format(i))
# update states
belief = pomdp.update_belief(belief, action, obs)
total_rewards += reward
budget -= cost
# print ino
log.info('\n'.join([
'Taking action: {}'.format(action),
'Observation: {}'.format(obs),
'Reward: {}'.format(reward),
'Budget: {}'.format(budget),
'New state: {}'.format(new_state),
'New Belief: {}'.format(belief), '=' * 20
]))
if budget <= 0:
log.info('Budget spent.')
log.info('{} games played. Total reward = {}'.format(
i + 1, total_rewards))
total_rewards_simulations.append(total_rewards)
exp_total_reward = np.mean(total_rewards_simulations)
std_exp_total_reward = np.std(total_rewards_simulations)
print(params.sim, 'simulations played.')
print('Exp total reward = ', exp_total_reward)
print('Std Exp total reward = ', std_exp_total_reward)
log.info('{} simulations played. Exp total reward = {}'.format(
params.sim, exp_total_reward))
log.info('Total rewards observed = {}'.format(
total_rewards_simulations))
return pomdp