from introrl.dp_funcs.dp_policy_iter import dp_policy_iteration
from introrl.policy import Policy
from introrl.state_values import StateValues
from introrl.mdp_data.car_rental import get_env
from introrl.utils import pickle_esp
env = get_env()
policy = Policy(environment=env)
policy.intialize_policy_to_random(env=env)
state_value = StateValues(env)
state_value.init_Vs_to_zero()
dp_policy_iteration(policy,
state_value,
do_summ_print=True,
show_start_policy=True,
max_iter=1000,
err_delta=0.0001,
gamma=0.9)
diag_colorD = {
'5': 'r',
'4': 'g',
'3': 'b',
'2': 'c',
'1': 'y',
'0': 'w',
'-5': 'r',
'-4': 'g',
class MyTest(unittest.TestCase):
def setUp(self):
unittest.TestCase.setUp(self)
self.gridworld = get_gridworld()
self.P = Policy(environment=self.gridworld)
self.P.intialize_policy_to_equiprobable(env=self.gridworld)
def tearDown(self):
unittest.TestCase.tearDown(self)
del (self.P)
def test_should_always_pass_cleanly(self):
"""Should always pass cleanly."""
pass
def test_myclass_existence(self):
"""Check that myclass exists"""
# See if the self.P object exists
self.assertIsInstance(self.P, Policy, msg=None)
def test_set_policy_from_default_pi(self):
"""test set policy from default pi"""
policyD = self.gridworld.get_default_policy_desc_dict()
self.P.set_policy_from_piD(policyD)
self.assertEqual(self.P.get_action_prob((2, 2), 'U'), 1.0)
self.assertEqual(self.P.get_action_prob((2, 2), 'R'), 0.0)
self.assertEqual(self.P.get_action_prob((2, 2), 'D'), None)
#def test_set_policy_from_list_of_actions(self):
# """test set policy from list of actions"""
# piD = {(0, 0):('R','D') }
# self.P.set_policy_from_piD( piD )
# self.assertEqual(self.P.get_action_prob( (0,0), 'U'), None)
# self.assertEqual(self.P.get_action_prob( (0,0), 'R'), 0.5)
# self.assertEqual(self.P.get_action_prob( (0,0), 'D'), 0.5)
#def test_set_policy_from_list_of_action_probs(self):
# """test set policy from list of action probs"""
# piD = {(0, 0):[('R',0.6), ('D',0.4)] }
# self.P.set_policy_from_piD( piD )
# self.assertEqual(self.P.get_action_prob( (0,0), 'U'), None)
# self.assertEqual(self.P.get_action_prob( (0,0), 'R'), 0.6)
# self.assertEqual(self.P.get_action_prob( (0,0), 'D'), 0.4)
# # make (action, prob) entry too long.
# with self.assertRaises(ValueError):
# piD = {(0, 0):[('R',0.6,0.4), ('D',0.4,0.6)] }
# self.P.set_policy_from_piD( piD )
def test_learn_all_s_and_a(self):
"""test learn all s and a"""
self.P.learn_all_states_and_actions_from_env(self.gridworld)
def test_initialize_to_random(self):
"""test initialize to random"""
self.P.intialize_policy_to_random(env=self.gridworld)
apL = self.P.get_list_of_all_action_desc_prob((0, 2),
incl_zero_prob=True)
pL = [p for (adesc, p) in apL]
self.assertEqual(sorted(pL), [0.0, 0.0, 1.0])
def test_iterate_adesc_p(self):
"""test iterate adesc p"""
apL = []
for (a_desc, p) in self.P.iter_policy_ap_for_state(
(0, 0), incl_zero_prob=False):
apL.append((a_desc, p))
self.assertIn(('R', 0.5), apL)
self.assertIn(('D', 0.5), apL)
self.assertNotIn(('U', 0.5), apL)
def test_iterate_all_states(self):
"""test iterate all states"""
sL = []
for s_hash in self.P.iter_all_policy_states():
sL.append(s_hash)
sL.sort()
self.assertEqual(len(sL), 9)
self.assertEqual(sL[0], (0, 0))
self.assertEqual(sL[-1], (2, 3))
def test_get_single_action(self):
"""test get single action"""
a_desc = self.P.get_single_action((0, 0))
self.assertIn(a_desc, ('R', 'D'))
a_desc = self.P.get_single_action((99, 99))
self.assertEqual(a_desc, None)
verbosity=0,
show_env_states=False)
if __name__ == "__main__": # pragma: no cover
import sys
from introrl.policy import Policy
from introrl.state_values import StateValues
from introrl.dp_funcs.dp_policy_eval import dp_policy_evaluation
from introrl.mdp_data.simple_grid_world import get_gridworld
gridworld = get_gridworld()
pi = Policy(environment=gridworld)
#pi.intialize_policy_to_equiprobable(env=gridworld)
pi.intialize_policy_to_random(env=gridworld)
#pi.learn_all_states_and_actions_from_env( gridworld )
#pi.set_policy_from_piD( gridworld.get_default_policy_desc_dict() )
# change one action from gridworld default
pi.set_sole_action((1, 0), 'D') # is 'U' in default
sv = StateValues(gridworld)
sv.init_Vs_to_zero()
dp_policy_iteration(pi,
sv,
do_summ_print=True,
show_each_policy_change=True,
max_iter=1000,
def mc_exploring_starts(environment,
initial_policy='default',
read_pickle_file='',
save_pickle_file='',
first_visit=True,
do_summ_print=True,
showRunningAve=False,
fmt_Q='%g',
fmt_R='%g',
show_initial_policy=True,
max_num_episodes=1000,
min_num_episodes=10,
max_abserr=0.001,
gamma=0.9,
max_episode_steps=10000,
iteration_prints=0):
"""
... GIVEN AN ENVIRONMENT ...
apply Monte Carlo Exploring Starts to find the OPTIMAL POLICY
initial_policy can be 'default', 'random', policy_dictionary, Policy object
Returns: Policy and ActionValueRunAveColl objects
Use Episode Discounted Returns to find Q(s,a), Action-Value Function
Terminates when abserr < max_abserr
Assume that Q(s,a), action_value_ave, has been initialized prior to call.
Assume environment attached to policy will have method "get_any_action_state_hash"
in order to begin at any action state.
CREATES BOTH policy AND action_value OBJECTS.
"""
# create Policy and ActionValueRunAveColl objects
policy = Policy(environment=environment)
if initial_policy == 'default':
print('Initializing Policy to "default" in mc_exploring_starts')
policy.learn_a_legal_action_from_env(env=environment)
policy.set_policy_from_piD(environment.get_default_policy_desc_dict())
elif initial_policy == 'random':
print('Initializing Policy to "random" in mc_exploring_starts')
policy.intialize_policy_to_random(env=environment)
elif isinstance(initial_policy, Policy):
policy = initial_policy
else:
print('Initializing Policy to "custom policy" in mc_exploring_starts')
policy.learn_a_legal_action_from_env(env=environment)
policy.set_policy_from_piD(initial_policy)
action_value_ave = ActionValueRunAveColl(environment)
action_value_ave.init_Qsa_to_zero(
) # Terminal states w/o an action are NOT included
#action_value_ave.summ_print()
if read_pickle_file:
policy.init_from_pickle_file(read_pickle_file)
action_value_ave.init_from_pickle_file(read_pickle_file)
if do_summ_print:
if show_initial_policy:
print(
'=============== STARTING WITH THE INITIAL POLICY ===================='
)
policy.summ_print(verbosity=0,
environment=environment,
show_env_states=False,
none_str='*')
s = 'Starting a Maximum of %i Monte Carlo Exploring Start Episodes\nfor "%s" with Gamma = %g'%\
(max_num_episodes, environment.name, gamma)
banner(s, banner_char='', leftMargin=0, just='center')
# create an Episode object for getting returns
episode = Episode(environment.name + ' Episode')
# set counter and flag
num_episodes = 0
keep_looping = True
progress_str = ''
while (num_episodes <= max_num_episodes - 1) and keep_looping:
keep_looping = False
abserr = 0.0 # calculated below as part of termination criteria
for start_hash in environment.iter_all_action_states(randomize=True):
a_descL = environment.get_state_legal_action_list(start_hash)
# randomize action order
random.shuffle(a_descL)
# try every initial action for each start_hash
for a_desc in a_descL:
# break from inner loop if max_num_episodes is hit.
if num_episodes >= max_num_episodes:
break
make_episode(start_hash,
policy,
environment,
environment.terminal_set,
episode=episode,
first_a_desc=a_desc,
max_steps=max_episode_steps,
eps_greedy=None)
num_episodes += 1
for dr in episode.get_rev_discounted_returns(
gamma=gamma, first_visit=first_visit, visit_type='SA'):
# look at each step from episode and calc average Q(s,a)
(s, a, r, sn, G) = dr
action_value_ave.add_val(s, a, G)
aL = environment.get_state_legal_action_list(s)
if aL:
best_a_desc, best_a_val = aL[0], float('-inf')
bestL = [best_a_desc]
for a in aL:
q = action_value_ave.get_ave(s, a)
if q > best_a_val:
best_a_desc, best_a_val = a, q
bestL = [a]
elif q == best_a_val:
bestL.append(a)
best_a_desc = random.choice(bestL)
policy.set_sole_action(s, best_a_desc)
abserr = action_value_ave.get_biggest_action_state_err()
if abserr > max_abserr:
keep_looping = True
if num_episodes < min_num_episodes:
keep_looping = True # must loop for min_num_episodes at least
pc_done = 100.0 * float(num_episodes) / float(max_num_episodes)
out_str = '%3i%%' % (5 * (int(pc_done / 5.0)))
if out_str != progress_str:
score = environment.get_policy_score(policy=policy,
start_state_hash=None,
step_limit=1000)
print(out_str, ' score=%s' % str(score),
' = (r_sum, n_steps, msg)', ' estimated err =', abserr)
progress_str = out_str
if do_summ_print:
s = ''
if num_episodes >= max_num_episodes:
s = ' (NOTE: STOPPED ON MAX-ITERATIONS)'
print('Exited MC First-Visit Value Iteration', s)
print(' num episodes =', num_episodes,
' (min limit=%i)' % min_num_episodes,
' (max limit=%i)' % max_num_episodes)
print(' gamma =', gamma)
print(' estimated err =', abserr)
print(' Error limit =', max_abserr)
action_value_ave.summ_print(showRunningAve=showRunningAve, fmt_Q=fmt_Q)
policy.summ_print(environment=environment,
verbosity=0,
show_env_states=False)
try: # sims may not have a layout_print
environment.layout_print(vname='reward',
fmt=fmt_R,
show_env_states=False,
none_str='*')
except:
pass
if save_pickle_file:
policy.save_to_pickle_file(save_pickle_file)
action_value_ave.save_to_pickle_file(save_pickle_file)
return policy, action_value_ave
def dp_value_iteration(environment,
allow_multi_actions=False,
do_summ_print=True,
fmt_V='%g',
fmt_R='%g',
max_iter=1000,
err_delta=0.001,
gamma=0.9,
iteration_prints=0):
"""
... GIVEN AN ENVIRONMENT ...
apply Value Iteration to find the OPTIMAL POLICY
Returns: policy and state_value objects
Terminates when delta < err_delta * VI_STOP_CRITERIA
CREATES BOTH policy AND state_value OBJECTS.
If allow_multi_actions is True, policy will include all actions
within err_delta of best action.
"""
# create Policy and StateValues objects
policy = Policy(environment=environment)
policy.intialize_policy_to_random(env=environment)
state_value = StateValues(environment)
state_value.init_Vs_to_zero() # Terminal states need to be 0.0
#state_value.summ_print()
# set counter and flag
loop_counter = 0
all_done = False
# value-iteration stopping criteria
# if gamme==1.0 value iteration will never stop SO limit to gamma==0.999 stop criteria
# (VI terminates if delta < err_delta * VI_STOP_CRITERIA)
# (typically err_delta = 0.001)
VI_STOP_CRITERIA = max((1.0 - gamma) / gamma, (1.0 - 0.999) / 0.999)
error_limit = err_delta * VI_STOP_CRITERIA
while (loop_counter < max_iter) and (not all_done):
loop_counter += 1
all_done = True
delta = 0.0 # used to calc largest change in state_value
for s_hash in policy.iter_all_policy_states():
VsD = {
} # will hold: index=a_desc, value=V(s) for all transitions of a_desc from s_hash
# MUST include currently zero prob actions
for a_desc, a_prob in policy.iter_policy_ap_for_state(
s_hash, incl_zero_prob=True):
calcd_v = 0.0
for sn_hash, t_prob, reward in \
environment.iter_next_state_prob_reward(s_hash, a_desc, incl_zero_prob=False):
calcd_v += t_prob * (reward + gamma * state_value(sn_hash))
VsD[a_desc] = calcd_v
best_a_desc, best_a_val = argmax_vmax_dict(VsD)
delta = max(delta, abs(best_a_val - state_value(s_hash)))
state_value[s_hash] = best_a_val
if delta > error_limit:
all_done = False
if iteration_prints and (loop_counter % iteration_prints == 0):
print('Loop:%6i' % loop_counter, ' delta=%g' % delta)
# Now that State-Values have been determined, set policy
for s_hash in policy.iter_all_policy_states():
VsD = {
} # will hold: index=a_desc, value=V(s) for all transitions of a_desc from s_hash
# MUST include zero prob actions
for a_desc, a_prob in policy.iter_policy_ap_for_state(
s_hash, incl_zero_prob=True):
calcd_v = 0.0
for sn_hash, t_prob, reward in \
environment.iter_next_state_prob_reward(s_hash, a_desc, incl_zero_prob=False):
calcd_v += t_prob * (reward + gamma * state_value(sn_hash))
VsD[a_desc] = calcd_v
if allow_multi_actions:
best_a_list, best_a_val = multi_argmax_vmax_dict(
VsD, err_delta=err_delta)
policy.set_sole_action(s_hash,
best_a_list[0]) # zero all other actions
prob = 1.0 / len(best_a_list)
for a_desc in best_a_list:
policy.set_action_prob(s_hash, a_desc, prob=prob)
else:
best_a_desc, best_a_val = argmax_vmax_dict(VsD)
policy.set_sole_action(s_hash, best_a_desc)
if do_summ_print:
s = ''
if loop_counter >= max_iter:
s = ' (NOTE: STOPPED ON MAX-ITERATIONS)'
print('Exited Value Iteration', s)
print(' iterations =', loop_counter, ' (limit=%i)' % max_iter)
print(' measured delta =', delta)
print(' gamma =', gamma)
print(' err_delta =', err_delta)
print(' error limit =', error_limit)
print(' STOP CRITERIA =', VI_STOP_CRITERIA)
state_value.summ_print(fmt_V=fmt_V)
policy.summ_print(environment=environment,
verbosity=0,
show_env_states=False)
environment.layout_print(vname='reward',
fmt=fmt_R,
show_env_states=False,
none_str='*')
return policy, state_value
