1 if a_desc == 'R' else 0, s[1] - 1.5 if a_desc == 'R' else 0,
(s[0] * s[1] - 3) / 3 if a_desc == 'R' else 0,
(s[0] * s[0] - 2) / 2 if a_desc == 'R' else 0,
(s[1] * s[1] - 4.5) / 4.5 if a_desc == 'R' else 0,
1 if a_desc == 'R' else 0, 1
])
return x_vector
learn_tracker = LearnTracker()
gridworld = get_gridworld(step_reward=-0.1)
NUM_EPISODES = 20000
alpha_obj = Alpha(alpha=0.1)
alpha_obj.set_half_life_for_N_episodes(Nepisodes=NUM_EPISODES,
alpha_final=0.03333333333333)
eps_obj = EpsilonGreedy(epsilon=0.5)
eps_obj.set_half_life_for_N_episodes(Nepisodes=NUM_EPISODES,
epsilon_final=0.16666666666666)
agent = SA_SemiGradAgent(environment=gridworld,
update_type='qlearn',
sa_linear_function=LazyProgrammerMaze(gridworld),
learn_tracker=learn_tracker,
gamma=0.9,
alpha=alpha_obj,
epsilon=eps_obj)
import numpy as np
from introrl.black_box_sims.random_walk_1000 import RandomWalk_1000Simulation
from introrl.agent_supt.episode_maker import make_episode
from introrl.policy import Policy
from introrl.utils.tiles_rectangles import PartitionedSegment
from introrl.agent_supt.alpha_calc import Alpha
NUM_EPISODES = 100000
RW = RandomWalk_1000Simulation()
policy = Policy(environment=RW)
policy.intialize_policy_to_equiprobable(env=RW)
alpha_obj = Alpha(alpha=0.01)
alpha_obj.set_half_life_for_N_episodes(Nepisodes=NUM_EPISODES,
alpha_final=2.0E-5)
pseg = PartitionedSegment(lo_val=0, hi_val=1000, num_regions=10)
#pseg.summ_print()
def get_x_vector(state):
"""Return the x vector that represents the state."""
x_vector = pseg.get_numpy_encoding(state)
return x_vector
def VsEst(state):
"""Return the current estimate for V(s) from linear function eval."""
x_vector = get_x_vector(state)
def td0_prediction(
policy,
state_value_coll,
all_start_states=False,
do_summ_print=True,
show_last_change=True,
alpha=0.1,
const_alpha=True,
alpha_half_life=200,
max_num_episodes=1000,
min_num_episodes=10,
max_abserr=0.001,
gamma=1.0,
result_list='abserr',
true_valueD=None,
value_snapshot_loopL=None
): # if input, save V(s) snapshot at iteration steps indicated
"""
... GIVEN A POLICY TO EVALUATE apply TD(0), Temperal Difference(0) Prediction
Terminates when abserr < max_abserr
Assume that Q(s,a), action_value_coll, has been initialized prior to call.
(Note tht the StateValues object has a reference to the Environment object)
Assume environment attached to policy will have method "get_any_action_state_hash"
in order to begin at any start state.
action_value_coll WILL BE CHANGED... policy WILL NOT.
"""
resultL = [] # based on result_list, can be "rms" or "abserr"
value_snapD = {} # index=loop counter, value=dict of {s_hash:Vs, ...}
# ==> Note: the reference to Environment object as "state_value_coll.environment"
Env = state_value_coll.environment
alpha_obj = Alpha(alpha=alpha,
const_alpha=const_alpha,
half_life=alpha_half_life)
if do_summ_print:
print(
'=============== TD(0) EVALUATING THE FOLLOWING POLICY ===================='
)
policy.summ_print(verbosity=0,
environment=Env,
show_env_states=False,
none_str='*')
if all_start_states:
s = 'Starting a Maximum of %i TD(0) All-Start-State Episodes\nGamma = %g'%\
(max_num_episodes, gamma)
start_stateL = [s_hash for s_hash in Env.iter_all_action_states()]
else:
s = 'Starting a Maximum of %i TD(0) Episodes from state "%s"\nGamma = %g'%\
(max_num_episodes, str(Env.start_state_hash), gamma)
start_stateL = [Env.start_state_hash]
banner(s, banner_char='', leftMargin=0, just='center')
num_episodes = 0
keep_looping = True
# value-iteration stopping criteria
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
# policy evaluation
for start_hash in start_stateL:
num_episodes += 1
if num_episodes > max_num_episodes:
break
s_hash = start_hash
a_desc = policy.get_single_action(s_hash)
for _ in range(max_num_episodes):
sn_hash, reward = Env.get_action_snext_reward(
s_hash, a_desc) # prob-weighted choice
state_value_coll.td0_update(s_hash=s_hash,
alpha=alpha_obj(),
gamma=gamma,
sn_hash=sn_hash,
reward=reward)
if (sn_hash in Env.terminal_set) or (sn_hash is None):
break
# get ready for next step
s_hash = sn_hash
a_desc = policy.get_single_action(s_hash)
if a_desc is None:
print('a_desc is None for policy.get_single_action( "%s" ) ='%\
str(s_hash), a_desc)
abserr = state_value_coll.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 = '%i%%' % (5 * (int(pc_done / 5.0)))
if out_str != progress_str:
print(out_str, end=' ')
progress_str = out_str
if result_list == 'rms':
resultL.append(state_value_coll.calc_rms_error(true_valueD))
if result_list == 'abserr':
resultL.append(abserr)
else:
pass # don't save anything to resultL
if value_snapshot_loopL is not None and num_episodes in value_snapshot_loopL:
value_snapD[num_episodes] = state_value_coll.get_snapshot()
if do_summ_print:
s = ''
if num_episodes >= max_num_episodes:
s = ' (NOTE: STOPPED ON MAX-ITERATIONS)'
print('Exited MC Every-Visit Policy Evaluation', 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)
state_value_coll.summ_print(show_last_change=show_last_change,
show_states=True)
return resultL, value_snapD
def mc_every_visit_prediction(
policy,
state_value_coll,
all_start_states=False,
do_summ_print=True,
show_last_change=True,
show_banner=True,
max_episode_steps=10000,
alpha=0.1,
const_alpha=True,
alpha_half_life=200,
max_num_episodes=1000,
min_num_episodes=10,
max_abserr=0.001,
gamma=0.9,
result_list='abserr',
true_valueD=None,
value_snapshot_loopL=None
): # if input, save V(s) snapshot at iteration steps indicated
"""
... GIVEN A POLICY TO EVALUATE apply Monte Carlo Every Visit Prediction
Use Episode Discounted Returns to find V(s), State-Value Function
Terminates when abserr < max_abserr
Assume that V(s), state_value_coll, has been initialized prior to call.
(Note tht the StateValues object has a reference to the Environment object)
Assume environment attached to policy will have method "get_any_action_state_hash"
in order to begin at any start state.
state_value_coll WILL BE CHANGED... policy WILL NOT.
"""
resultL = [] # based on result_list, can be "rms" or "abserr"
value_snapD = {} # index=loop counter, value=dict of {s_hash:Vs, ...}
# ==> Note: the reference to Environment object as "state_value_coll.environment"
Env = state_value_coll.environment
episode = Episode(Env.name + ' Episode')
alpha_obj = Alpha(alpha=alpha,
const_alpha=const_alpha,
half_life=alpha_half_life)
if do_summ_print:
print(
'=============== EVALUATING THE FOLLOWING POLICY ===================='
)
policy.summ_print(verbosity=0,
environment=Env,
show_env_states=False,
none_str='*')
if all_start_states:
s = 'Starting a Maximum of %i Monte Carlo All-Start-State Iterations\nGamma = %g' % (
max_num_episodes, gamma)
start_stateL = [s_hash for s_hash in Env.iter_all_action_states()]
else:
s = 'Starting a Maximum of %i Monte Carlo Iterations from state "%s"\nGamma = %g' % (
max_num_episodes, str(Env.start_state_hash), gamma)
start_stateL = [Env.start_state_hash]
if show_banner:
banner(s, banner_char='', leftMargin=0, just='center')
num_episodes = 0
keep_looping = True
# value-iteration stopping criteria
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
# policy evaluation
random.shuffle(start_stateL)
for start_hash in start_stateL:
# break from inner loop if max_num_episodes is hit.
if num_episodes >= max_num_episodes:
break
make_episode(start_hash,
policy,
Env,
Env.terminal_set,
episode=episode,
max_steps=max_episode_steps,
eps_greedy=None)
num_episodes += 1
for dr in episode.get_rev_discounted_returns(gamma=gamma):
(s_hash, a_desc, reward, sn_hash, G) = dr
state_value_coll.mc_update(s_hash, alpha_obj(), G)
abserr = state_value_coll.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 = '%i%%' % (5 * (int(pc_done / 5.0)))
if out_str != progress_str:
print(out_str, end=' ')
progress_str = out_str
if result_list == 'rms':
resultL.append(state_value_coll.calc_rms_error(true_valueD))
if result_list == 'abserr':
resultL.append(abserr)
else:
pass # don't save anything to resultL
if value_snapshot_loopL is not None and num_episodes in value_snapshot_loopL:
value_snapD[num_episodes] = state_value_coll.get_snapshot()
if do_summ_print:
s = ''
if num_episodes >= max_num_episodes:
s = ' (NOTE: STOPPED ON MAX-ITERATIONS)'
print('Exited MC Every-Visit Policy Evaluation', 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)
state_value_coll.summ_print(show_last_change=show_last_change,
show_states=True)
return resultL, value_snapD
def sarsa_epsilon_greedy( environment, learn_tracker=None, # track progress of learning
initial_Qsa=0.0, # init non-terminal_set of V(s) (terminal_set=0.0)
initial_action_value_coll=None, # if input, use it.
read_pickle_file='',
save_pickle_file='',
use_list_of_start_states=False, # use list OR single start state of environment.
do_summ_print=True, show_last_change=True, fmt_Q='%g', fmt_R='%g',
pcent_progress_print=10,
show_banner = True,
max_num_episodes=1000, min_num_episodes=10, max_abserr=0.001,
gamma=0.9,
iteration_prints=0,
max_episode_steps=sys.maxsize,
epsilon=0.1, const_epsilon=True, epsilon_half_life=200,
alpha=0.1, const_alpha=True, alpha_half_life=200,
N_episodes_wo_decay=0):
"""
... GIVEN AN ENVIRONMENT ...
apply SARSA Temporal Difference to find the OPTIMAL POLICY and STATE VALUES
Returns: Policy and ActionValueColl objects
Use Episode Discounted Returns to find V(s), State-Value Function
Terminates when abserr < max_abserr
Assume that V(s), action_value_coll, 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_coll OBJECTS.
"""
# create EpsilonGreedy, Alpha and ActionValueColl objects
eg = EpsilonGreedy(epsilon=epsilon, const_epsilon=const_epsilon, half_life=epsilon_half_life,
N_episodes_wo_decay=N_episodes_wo_decay)
alpha_obj = Alpha( alpha=alpha, const_alpha=const_alpha, half_life=alpha_half_life )
if initial_action_value_coll is None:
action_value_coll = ActionValueColl( environment, init_val=initial_Qsa )
else:
action_value_coll = initial_action_value_coll
#action_value_coll.summ_print()
num_s_hash = len( environment.get_all_action_state_hashes() )
if read_pickle_file:
action_value_coll.init_from_pickle_file( read_pickle_file )
if do_summ_print:
print('================== EPSILON GREEDY DEFINED AS ========================')
eg.summ_print()
print('================== LEARNING RATE DEFINED AS ========================')
alpha_obj.summ_print()
if show_banner:
s = 'Starting a Maximum of %i SARSA Epsilon Greedy Episodes'%max_num_episodes +\
'\nfor "%s" with Gamma = %g, Alpha = %g'%( environment.name, gamma, alpha_obj() )
banner(s, banner_char='', leftMargin=0, just='center')
# Iterate over a list of known possible start states
if use_list_of_start_states:
loop_stateL = environment.limited_start_state_list()
else:
#loop_stateL = [ random.choice( environment.limited_start_state_list() ) ]
loop_stateL = [ environment.start_state_hash ]
if show_banner:
print('======================= Iterating over Start States ==================================')
print( loop_stateL )
print('======================================================================================')
# set counter and flag
episode_loop_counter = 0
keep_looping = True
progress_str = ''
while (episode_loop_counter<=max_num_episodes-1) and keep_looping :
keep_looping = False
abserr = 0.0 # calculated below as part of termination criteria
Nterminal_episodes = set() # tracks if start_hash got to terminal_set or max_num_episodes
for start_hash in loop_stateL:
episode_loop_counter += 1
if episode_loop_counter > max_num_episodes:
break
if learn_tracker is not None:
learn_tracker.add_new_episode()
s_hash = start_hash
a_desc = action_value_coll.get_best_eps_greedy_action( s_hash, epsgreedy_obj=eg )
for n_episode_steps in range( max_episode_steps ):
# Begin an episode
if a_desc is None:
Nterminal_episodes.add( start_hash )
print('break for a_desc==None')
break
else:
sn_hash, reward = environment.get_action_snext_reward( s_hash, a_desc )
if learn_tracker is not None:
learn_tracker.add_sarsn_to_current_episode( s_hash, a_desc,
reward, sn_hash)
if sn_hash is None:
Nterminal_episodes.add( start_hash )
print('break for sn_hash==None')
break
else:
an_desc = action_value_coll.get_best_eps_greedy_action( sn_hash,
epsgreedy_obj=eg )
action_value_coll.sarsa_update( s_hash=s_hash, a_desc=a_desc,
alpha=alpha_obj(), gamma=gamma,
sn_hash=sn_hash, an_desc=an_desc,
reward=reward)
if sn_hash in environment.terminal_set:
Nterminal_episodes.add( start_hash )
if (n_episode_steps==0) and (num_s_hash>2):
print('1st step break for sn_hash in terminal_set', sn_hash,
' s_hash=%s'%str(s_hash), ' a_desc=%s'%str(a_desc))
break
s_hash = sn_hash
a_desc = an_desc
# increment episode counter on EpsilonGreedy and Alpha objects
eg.inc_N_episodes()
alpha_obj.inc_N_episodes()
abserr = action_value_coll.get_biggest_action_state_err()
if abserr > max_abserr:
keep_looping = True
if episode_loop_counter < min_num_episodes:
keep_looping = True # must loop for min_num_episodes at least
pc_done = 100.0 * float(episode_loop_counter) / float(max_num_episodes)
if pcent_progress_print > 0:
out_str = '%3i%%'%( pcent_progress_print*(int(pc_done/float(pcent_progress_print)) ) )
else:
out_str = progress_str
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)', end=' ')
print(out_str, end=' ')
print( 'Nterminal episodes =', len(Nterminal_episodes),' of ', len(loop_stateL))
progress_str = out_str
#print()
policy = action_value_coll.get_policy()
if do_summ_print:
s = ''
if episode_loop_counter >= max_num_episodes:
s = ' (NOTE: STOPPED ON MAX-ITERATIONS)'
print( 'Exited Epsilon Greedy, TD(0) Value Iteration', s )
print( ' # episodes =', episode_loop_counter, ' (min limit=%i)'%min_num_episodes, ' (max limit=%i)'%max_num_episodes )
print( ' gamma =', gamma )
print( ' estimated err =', abserr )
print( ' Error limit =', max_abserr )
print( 'Nterminal episodes =', len(Nterminal_episodes),' of ', len(loop_stateL))
action_value_coll.summ_print(show_last_change=show_last_change, 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
print('================== EPSILON GREEDY DEFINED AS ========================')
eg.summ_print()
print('================== LEARNING RATE DEFINED AS ========================')
alpha_obj.summ_print()
if save_pickle_file:
policy.save_to_pickle_file( save_pickle_file )
action_value_coll.save_to_pickle_file( save_pickle_file )
return policy, action_value_coll #, steps_per_episodeL, reward_sum_per_episodeL
def __init__(
self,
environment,
learn_tracker=None, # track progress of learning
sa_linear_function=None, # if input, use it.
update_type='sarsa', # can be 'sarsa', 'qlearn'
read_pickle_file='',
save_pickle_file='',
do_summ_print=True,
show_last_change=True,
pcent_progress_print=10,
show_banner=True,
gamma=0.9,
iteration_prints=0,
max_episode_steps=sys.maxsize,
epsilon=0.1, # can be constant or EpsilonGreedy object
alpha=0.1): # can be constant or Alpha object
"""
... GIVEN AN ENVIRONMENT ...
Use basic SARSA or Qlearning algorithm to solve for linear approximation of
STATE-ACTION VALUES, Q(s,a)
Each action is forced to be a DETERMINISTIC action leading to one state and reward.
(If the next state or reward changes, only the new values will be considered)
attribute: self.action_value_linfunc is the linear approximation, Q(s,a) object
A DETERMINISTIC policy can be created externally from the self.action_value_linfunc attribute.
"""
self.environment = environment
self.learn_tracker = learn_tracker
self.save_pickle_file = save_pickle_file
self.do_summ_print = do_summ_print
self.show_last_change = show_last_change
self.pcent_progress_print = pcent_progress_print
self.gamma = gamma
self.iteration_prints = iteration_prints
self.max_episode_steps = max_episode_steps
self.num_episodes = 0
self.num_updates = 0
# if input epsilon is a float, use it to create an EpsilonGreedy object
if type(epsilon) == type(0.1):
self.epsilon_obj = EpsilonGreedy(epsilon=epsilon,
const_epsilon=True)
else:
self.epsilon_obj = epsilon
# if input alpha is a float, use it to create an Alpha object
if type(alpha) == type(0.1):
self.alpha_obj = Alpha(alpha=alpha, const_alpha=True)
else:
self.alpha_obj = alpha
# create the action_value_linfunc for the environment.
self.action_value_linfunc = sa_linear_function
self.update_type = update_type
if read_pickle_file:
self.action_value_linfunc.init_from_pickle_file(read_pickle_file)
if do_summ_print:
print(
'================== EPSILON GREEDY DEFINED AS ========================'
)
self.epsilon_obj.summ_print()
print(
'================== LEARNING RATE DEFINED AS ========================'
)
self.alpha_obj.summ_print()
if show_banner:
s = 'Starting a Maximum of %i %s Semi-Gradient Epsilon Greedy Steps/Episode'%(self.max_episode_steps, update_type.upper()) +\
'\nfor "%s" with Gamma = %g, Alpha = %g'%( environment.name, self.gamma, self.alpha_obj() )
banner(s, banner_char='', leftMargin=0, just='center')
class SA_SemiGradAgent(object):
"""
SARSA or Qlearning semi-gradient agent for linear function approximator.
"""
def __init__(
self,
environment,
learn_tracker=None, # track progress of learning
sa_linear_function=None, # if input, use it.
update_type='sarsa', # can be 'sarsa', 'qlearn'
read_pickle_file='',
save_pickle_file='',
do_summ_print=True,
show_last_change=True,
pcent_progress_print=10,
show_banner=True,
gamma=0.9,
iteration_prints=0,
max_episode_steps=sys.maxsize,
epsilon=0.1, # can be constant or EpsilonGreedy object
alpha=0.1): # can be constant or Alpha object
"""
... GIVEN AN ENVIRONMENT ...
Use basic SARSA or Qlearning algorithm to solve for linear approximation of
STATE-ACTION VALUES, Q(s,a)
Each action is forced to be a DETERMINISTIC action leading to one state and reward.
(If the next state or reward changes, only the new values will be considered)
attribute: self.action_value_linfunc is the linear approximation, Q(s,a) object
A DETERMINISTIC policy can be created externally from the self.action_value_linfunc attribute.
"""
self.environment = environment
self.learn_tracker = learn_tracker
self.save_pickle_file = save_pickle_file
self.do_summ_print = do_summ_print
self.show_last_change = show_last_change
self.pcent_progress_print = pcent_progress_print
self.gamma = gamma
self.iteration_prints = iteration_prints
self.max_episode_steps = max_episode_steps
self.num_episodes = 0
self.num_updates = 0
# if input epsilon is a float, use it to create an EpsilonGreedy object
if type(epsilon) == type(0.1):
self.epsilon_obj = EpsilonGreedy(epsilon=epsilon,
const_epsilon=True)
else:
self.epsilon_obj = epsilon
# if input alpha is a float, use it to create an Alpha object
if type(alpha) == type(0.1):
self.alpha_obj = Alpha(alpha=alpha, const_alpha=True)
else:
self.alpha_obj = alpha
# create the action_value_linfunc for the environment.
self.action_value_linfunc = sa_linear_function
self.update_type = update_type
if read_pickle_file:
self.action_value_linfunc.init_from_pickle_file(read_pickle_file)
if do_summ_print:
print(
'================== EPSILON GREEDY DEFINED AS ========================'
)
self.epsilon_obj.summ_print()
print(
'================== LEARNING RATE DEFINED AS ========================'
)
self.alpha_obj.summ_print()
if show_banner:
s = 'Starting a Maximum of %i %s Semi-Gradient Epsilon Greedy Steps/Episode'%(self.max_episode_steps, update_type.upper()) +\
'\nfor "%s" with Gamma = %g, Alpha = %g'%( environment.name, self.gamma, self.alpha_obj() )
banner(s, banner_char='', leftMargin=0, just='center')
def run_episode(self, start_state, iter_sarsn=None):
"""
Run a single episode of SARSA or Qlearning Semi-Gradient algorithm
If iter_sarsn is input, use it instead of action_value_linfunc calculations.
(Note: the start_state should NOT be in terminal_set if iter_sarsn is input.)
"""
# increment episode counters
self.num_episodes += 1
self.epsilon_obj.inc_N_episodes()
self.alpha_obj.inc_N_episodes()
if self.learn_tracker is not None:
self.learn_tracker.add_new_episode()
# do SARSA or Qlearning Semi-Gradient loops until sn_hash in terminal_set
s_hash = start_state
n_steps_in_episode = 1
while s_hash not in self.environment.terminal_set:
if iter_sarsn is None:
# get best epsilon-greedy action
a_desc = self.action_value_linfunc.get_best_eps_greedy_action( \
s_hash, epsgreedy_obj=self.epsilon_obj )
# check for bad action value
if a_desc is None:
print('break for a_desc==None at s_hash=%s' % str(s_hash))
break
# get next state and reward
sn_hash, reward = self.environment.get_action_snext_reward(
s_hash, a_desc)
else:
# retracing an existing episode
s_hash, a_desc, reward, sn_hash = next(iter_sarsn)
if self.learn_tracker is not None:
self.learn_tracker.add_sarsn_to_current_episode(
s_hash, a_desc, reward, sn_hash)
if sn_hash is None:
print('break for sn_hash==None, #steps=', n_steps_in_episode,
' s_hash=%s' % str(s_hash), ' a_desc=%s' % str(a_desc))
break
# do RL update of Q(s,a) value
if self.update_type == 'sarsa':
an_desc = self.action_value_linfunc.get_best_eps_greedy_action( \
sn_hash, epsgreedy_obj=self.epsilon_obj )
self.action_value_linfunc.sarsa_update(s_hash=s_hash,
a_desc=a_desc,
alpha=self.alpha_obj(),
gamma=self.gamma,
sn_hash=sn_hash,
an_desc=an_desc,
reward=reward)
elif self.update_type == 'qlearn':
self.action_value_linfunc.qlearning_update(
s_hash=s_hash,
a_desc=a_desc,
alpha=self.alpha_obj(),
gamma=self.gamma,
sn_hash=sn_hash,
reward=reward)
self.num_updates += 1
# keep a lid on the max number of episode steps.
if n_steps_in_episode >= self.max_episode_steps:
break
# get ready for next loop
n_steps_in_episode += 1
s_hash = sn_hash
#print(n_steps_in_episode, end=' ')
def summ_print(self, long=True): # pragma: no cover
"""Show State objects in sorted state_hash order."""
print('___ Policy Evaluation Agent Summary ___')
print(' Environment = %s' % self.environment.name)
print(' Update Type = %s' % self.update_type)
print(' Number of Episodes = %g' % self.num_episodes)
print(
'================== EPSILON GREEDY FINAL ========================')
self.epsilon_obj.summ_print()
print(
'================== LEARNING RATE FINAL ========================')
self.alpha_obj.summ_print()
NUM_EPISODES = 5000
UPDATE_TYPE = 'sarsa'
GAMMA = 1.0 # 0.99
eg = EpsilonGreedy(epsilon=0.01,
const_epsilon=True,
half_life=200,
N_episodes_wo_decay=0)
eg.set_half_life_for_N_episodes(Nepisodes=NUM_EPISODES,
epsilon_final=0.00001)
#eg = None
ALPHA = 0.05
if hasattr(ff, 'num_tiles'):
alpha_obj = Alpha(alpha=ALPHA / ff.num_tiles)
else:
alpha_obj = Alpha(alpha=ALPHA)
#alpha_obj.set_half_life_for_N_episodes( Nepisodes=NUM_EPISODES, alpha_final=0.001)
ep_lenL = []
for loop_counter in range(NUM_EPISODES):
if loop_counter % 100 == 0:
print()
print('Loop:', loop_counter)
sim.reset()
x_init = -0.6 + 0.2 * random.random()
episode = run_episode((x_init, 0),
def __init__(
self,
environment,
learn_tracker=None, # track progress of learning
initial_Qsa=0.0, # init non-terminal_set of V(s) (terminal_set=0.0)
initial_action_value_coll=None, # if input, use it.
read_pickle_file='',
save_pickle_file='',
do_summ_print=True,
show_last_change=True,
pcent_progress_print=10,
show_banner=True,
gamma=0.9,
iteration_prints=0,
max_episode_steps=sys.maxsize,
epsilon=0.1, # can be constant or EpsilonGreedy object
alpha=0.1): # can be constant or Alpha object
"""
... GIVEN AN ENVIRONMENT ...
Use basic Dyna-Q algorithm to solve for STATE-ACTION VALUES, Q(s,a)
Each action is forced to be a DETERMINISTIC action leading to one state and reward.
(If the next state or reward changes, only the new values will be considered)
attribute: self.action_value_coll is the ActionValueColl, Q(s,a) object
A DETERMINISTIC policy can be created externally from the self.action_value_coll attribute.
"""
self.environment = environment
self.learn_tracker = learn_tracker
self.save_pickle_file = save_pickle_file
self.do_summ_print = do_summ_print
self.show_last_change = show_last_change
self.pcent_progress_print = pcent_progress_print
self.gamma = gamma
self.iteration_prints = iteration_prints
self.max_episode_steps = max_episode_steps
self.num_episodes = 0
self.num_updates = 0
# if input epsilon is a float, use it to create an EpsilonGreedy object
if type(epsilon) == type(0.1):
self.epsilon_obj = EpsilonGreedy(epsilon=epsilon,
const_epsilon=True)
else:
self.epsilon_obj = epsilon
# if input alpha is a float, use it to create an Alpha object
if type(alpha) == type(0.1):
self.alpha_obj = Alpha(alpha=alpha, const_alpha=True)
else:
self.alpha_obj = alpha
# create the action_value_coll for the environment.
if initial_action_value_coll is None:
self.action_value_coll = ActionValueColl(environment,
init_val=initial_Qsa)
else:
self.action_value_coll = initial_action_value_coll
if read_pickle_file:
self.action_value_coll.init_from_pickle_file(read_pickle_file)
# initialize the model that will build from experience
# do not build full model description on Model init, states not visited
# by the RL portion will have no returns values.
self.model = Model(environment, build_initial_model=False)
#for s_hash, aD in self.action_value_coll.QsaD.items():
# for a_desc, Q in aD.items():
# self.model.add_action( s_hash, a_desc )
if do_summ_print:
print(
'================== EPSILON GREEDY DEFINED AS ========================'
)
self.epsilon_obj.summ_print()
print(
'================== LEARNING RATE DEFINED AS ========================'
)
self.alpha_obj.summ_print()
if show_banner:
s = 'Starting a Maximum of %i Dyna-Q Epsilon Greedy Steps/Episode'%self.max_episode_steps +\
'\nfor "%s" with Gamma = %g, Alpha = %g'%( environment.name, self.gamma, self.alpha_obj() )
banner(s, banner_char='', leftMargin=0, just='center')
class DynaQAgent(object):
"""
DynaQ Agent.
"""
def __init__(
self,
environment,
learn_tracker=None, # track progress of learning
initial_Qsa=0.0, # init non-terminal_set of V(s) (terminal_set=0.0)
initial_action_value_coll=None, # if input, use it.
read_pickle_file='',
save_pickle_file='',
do_summ_print=True,
show_last_change=True,
pcent_progress_print=10,
show_banner=True,
gamma=0.9,
iteration_prints=0,
max_episode_steps=sys.maxsize,
epsilon=0.1, # can be constant or EpsilonGreedy object
alpha=0.1): # can be constant or Alpha object
"""
... GIVEN AN ENVIRONMENT ...
Use basic Dyna-Q algorithm to solve for STATE-ACTION VALUES, Q(s,a)
Each action is forced to be a DETERMINISTIC action leading to one state and reward.
(If the next state or reward changes, only the new values will be considered)
attribute: self.action_value_coll is the ActionValueColl, Q(s,a) object
A DETERMINISTIC policy can be created externally from the self.action_value_coll attribute.
"""
self.environment = environment
self.learn_tracker = learn_tracker
self.save_pickle_file = save_pickle_file
self.do_summ_print = do_summ_print
self.show_last_change = show_last_change
self.pcent_progress_print = pcent_progress_print
self.gamma = gamma
self.iteration_prints = iteration_prints
self.max_episode_steps = max_episode_steps
self.num_episodes = 0
self.num_updates = 0
# if input epsilon is a float, use it to create an EpsilonGreedy object
if type(epsilon) == type(0.1):
self.epsilon_obj = EpsilonGreedy(epsilon=epsilon,
const_epsilon=True)
else:
self.epsilon_obj = epsilon
# if input alpha is a float, use it to create an Alpha object
if type(alpha) == type(0.1):
self.alpha_obj = Alpha(alpha=alpha, const_alpha=True)
else:
self.alpha_obj = alpha
# create the action_value_coll for the environment.
if initial_action_value_coll is None:
self.action_value_coll = ActionValueColl(environment,
init_val=initial_Qsa)
else:
self.action_value_coll = initial_action_value_coll
if read_pickle_file:
self.action_value_coll.init_from_pickle_file(read_pickle_file)
# initialize the model that will build from experience
# do not build full model description on Model init, states not visited
# by the RL portion will have no returns values.
self.model = Model(environment, build_initial_model=False)
#for s_hash, aD in self.action_value_coll.QsaD.items():
# for a_desc, Q in aD.items():
# self.model.add_action( s_hash, a_desc )
if do_summ_print:
print(
'================== EPSILON GREEDY DEFINED AS ========================'
)
self.epsilon_obj.summ_print()
print(
'================== LEARNING RATE DEFINED AS ========================'
)
self.alpha_obj.summ_print()
if show_banner:
s = 'Starting a Maximum of %i Dyna-Q Epsilon Greedy Steps/Episode'%self.max_episode_steps +\
'\nfor "%s" with Gamma = %g, Alpha = %g'%( environment.name, self.gamma, self.alpha_obj() )
banner(s, banner_char='', leftMargin=0, just='center')
def run_episode(self, start_state, Nplanning_loops=5, iter_sarsn=None):
"""
Run a single episode of Dyna-Q algorithm
If iter_sarsn is input, use it instead of action_value_coll calculations.
(Note: the start_state should NOT be in terminal_set if iter_sarsn is input.)
"""
# increment episode counters
self.num_episodes += 1
self.epsilon_obj.inc_N_episodes()
self.alpha_obj.inc_N_episodes()
if self.learn_tracker is not None:
self.learn_tracker.add_new_episode()
# do dyna_q loops until sn_hash in terminal_set
s_hash = start_state
n_steps_in_episode = 1
while s_hash not in self.environment.terminal_set:
if iter_sarsn is None:
# get best epsilon-greedy action
a_desc = self.action_value_coll.get_best_eps_greedy_action( \
s_hash, epsgreedy_obj=self.epsilon_obj )
# check for bad action value
if a_desc is None:
print('break for a_desc==None at s_hash=%s' % str(s_hash))
break
# get next state and reward
sn_hash, reward = self.environment.get_action_snext_reward(
s_hash, a_desc)
else:
# retracing an existing episode
s_hash, a_desc, reward, sn_hash = next(iter_sarsn)
if self.learn_tracker is not None:
self.learn_tracker.add_sarsn_to_current_episode(
s_hash, a_desc, reward, sn_hash)
if sn_hash is None:
print('break for sn_hash==None, #steps=', n_steps_in_episode,
' s_hash=%s' % str(s_hash), ' a_desc=%s' % str(a_desc))
break
# do RL update of Q(s,a) value
self.action_value_coll.qlearning_update(s_hash=s_hash,
a_desc=a_desc,
sn_hash=sn_hash,
alpha=self.alpha_obj(),
gamma=self.gamma,
reward=reward)
self.num_updates += 1
# give the above experience to the model
self.model.add_action(s_hash, a_desc)
# force DETERMINISTIC next state and reward.
self.model.save_deterministic_action_results(s_hash,
a_desc,
sn_hash,
reward_val=reward)
# do NOT use simple save_action_results... it allows NON-DETERMINISTIC next state.
#self.model.save_action_results( s_hash, a_desc, sn_hash, reward_val=reward)
# --------------------------------- Planning Loop ------------------------
# make Nplanning_loops calls to model
for n_plan in range(Nplanning_loops):
s_model = self.model.get_random_state()
#print(s_model, end=' ')
# vanilla DynaQ
a_model = self.model.get_random_action(s_model)
#sn_model, r_model = self.environment.get_action_snext_reward( s_model, a_model )
sn_model, r_model = self.model.get_sample_sn_r(
s_model, a_model)
# update for the DynaQ results.
self.action_value_coll.qlearning_update(s_hash=s_model,
a_desc=a_model,
sn_hash=sn_model,
alpha=self.alpha_obj(),
gamma=self.gamma,
reward=r_model)
self.num_updates += 1
# keep a lid on the max number of episode steps.
if n_steps_in_episode >= self.max_episode_steps:
break
# get ready for next loop
n_steps_in_episode += 1
s_hash = sn_hash
#print(n_steps_in_episode, end=' ')
def summ_print(self, long=True): # pragma: no cover
"""Show State objects in sorted state_hash order."""
print('___ Policy Evaluation Agent Summary ___')
print(' Environment = %s' % self.environment.name)
print(' Number of Episodes = %g' % self.num_episodes)