if 1:
pi, av = mc_epsilon_greedy(RT,
initial_policy='default',
first_visit=True,
do_summ_print=False,
showRunningAve=False,
fmt_Q='%g',
fmt_R='%g',
show_initial_policy=False,
max_num_episodes=1000,
min_num_episodes=10,
max_abserr=0.001,
gamma=0.9,
iteration_prints=0,
max_episode_steps=10000,
epsilon=0.1,
const_epsilon=True,
half_life=500,
N_episodes_wo_decay=0)
pi.save_to_pickle_file('racetrack_2_sim')
else:
pi = Policy(environment=RT)
pi.init_from_pickle_file('racetrack_2_sim')
fig, ax = plt.subplots()
RT.plot_policy(ax, pi)
plt.show()
fig.savefig("racetrack_2_sim.png")
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
