def __init__(self,
SubAgentClass,
actions,
state_abstr=None,
action_abstr=None,
name_ext="abstr"):
'''
Args:
SubAgentClass (simple_rl.AgentClass)
actions (list of str)
state_abstr (StateAbstraction)
state_abstr (ActionAbstraction)
name_ext (str)
'''
# Setup the abstracted agent.
self.agent = SubAgentClass(actions=actions)
self.action_abstr = ActionAbstraction(
options=self.agent.actions, prim_actions=self.agent.actions
) if action_abstr is None else action_abstr
self.state_abstr = StateAbstraction(
{}) if state_abstr is None else state_abstr
Agent.__init__(self,
name=self.agent.name + "-" + name_ext,
actions=self.action_abstr.get_actions())
def __init__(self, list_of_aa, prim_actions, level=0):
'''
Args:
list_of_aa (list)
'''
self.list_of_aa = list_of_aa
self.level = level
self.prim_actions = prim_actions
ActionAbstraction.__init__(self,
options=self.get_actions(level),
prim_actions=prim_actions)
def _create_default_abstractions(self, actions, state_abstr, action_abstr):
'''
Summary:
We here create the default abstractions.
'''
if action_abstr is None:
self.action_abstr = ActionAbstraction(options=actions,
prim_actions=actions)
else:
self.action_abstr = action_abstr
self.state_abstr = StateAbstraction(
phi={}) if state_abstr is None else state_abstr
def make_directed_options_aa_from_sa_stack(mdp_distr, sa_stack):
'''
Args:
mdp_distr (MDPDistribution)
sa_stack (StateAbstractionStack)
Returns:
(ActionAbstraction)
'''
aa_stack = ActionAbstractionStack(list_of_aa=[], prim_actions=mdp_distr.get_actions())
for level in xrange(1, sa_stack.get_num_levels() + 1):
# Make directed options for the current level.
sa_stack.set_level(level)
next_options = aa_helpers.get_directed_options_for_sa(mdp_distr, sa_stack, incl_self_loops=False)
if not next_options:
# Too many options, decrease abstracton ratio and continue.
return False
next_aa = ActionAbstraction(options=next_options, prim_actions=mdp_distr.get_actions())
aa_stack.add_aa(next_aa)
return aa_stack
def get_policy_blocks_aa(mdp_distr, num_options=10, task_samples=20, incl_prim_actions=False):
pb_options = make_policy_blocks_options(mdp_distr, num_options=num_options, task_samples=task_samples)
if type(mdp_distr) is dict:
first_mdp = mdp_distr.keys()[0]
else:
first_mdp = mdp_distr
if incl_prim_actions:
# Include the primitives.
aa = ActionAbstraction(options=first_mdp.get_actions(), prim_actions=first_mdp.get_actions())
for o in pb_options:
aa.add_option(o)
return aa
else:
# Return just the options.
return ActionAbstraction(options=pb_options, prim_actions=first_mdp.get_actions())
def get_directed_aa(mdp_distr,
state_abs,
incl_prim_actions=False,
max_options=100):
'''
Args:
mdp_distr (dict)
state_abs (StateAbstraction)
incl_prim_actions (bool)
max_options (int)
Returns:
(ActionAbstraction)
'''
directed_options = action_abs.aa_helpers.get_directed_options_for_sa(
mdp_distr, state_abs, incl_self_loops=True, max_options=max_options)
term_prob = 1 - mdp_distr.get_gamma()
if not directed_options:
# No good option set found.
return False
if incl_prim_actions:
# Include the primitives.
aa = ActionAbstraction(options=mdp_distr.get_actions(),
prim_actions=mdp_distr.get_actions(),
prims_on_failure=False,
term_prob=term_prob)
for o in directed_options:
aa.add_option(o)
return aa
else:
# Return just the options.
return ActionAbstraction(options=directed_options,
prim_actions=mdp_distr.get_actions(),
prims_on_failure=True,
term_prob=term_prob)
def get_aa(mdp_distr, default=False):
'''
Args:
mdp (defaultdict)
default (bool): If true, returns a blank ActionAbstraction
Returns:
(ActionAbstraction)
'''
if default:
return ActionAbstraction(options=mdp_distr.get_actions(),
prim_actions=mdp_distr.get_actions())
return action_abs.aa_helpers.make_greedy_options(mdp_distr)
def get_aa_high_prob_opt_single_act(mdp_distr, state_abstr, delta=0.2):
'''
Args:
mdp_distr
state_abstr (StateAbstraction)
Summary:
Computes an action abstraction where there exists an option that repeats a
single primitive action, for each primitive action that was optimal *with
high probability* in the ground state in the cluster.
'''
# K: state, V: dict (K: act, V: probability)
action_optimality_dict = state_abstr.get_act_opt_dict()
# Compute options.
options = []
for s_a in state_abstr.get_abs_states():
ground_states = state_abstr.get_ground_states_in_abs_state(s_a)
# One option per action.
for action in mdp_distr.get_actions():
list_of_state_with_a_optimal_high_pr = []
# Compute which states have high prob of being optimal.
for s_g in ground_states:
print "Pr(a = a^* \mid s_g)", s_g, action, action_optimality_dict[
s_g][action]
if action_optimality_dict[s_g][action] > (1 - delta):
list_of_state_with_a_optimal_high_pr.append(s_g)
if len(list_of_state_with_a_optimal_high_pr) == 0:
continue
init_predicate = ContainsPredicate(
list_of_items=list_of_state_with_a_optimal_high_pr)
term_predicate = NotPredicate(init_predicate)
policy_obj = Policy(action)
o = Option(init_predicate=init_predicate,
term_predicate=term_predicate,
policy=policy_obj.get_action)
options.append(o)
return ActionAbstraction(options=options,
prim_actions=mdp_distr.get_actions(),
prims_on_failure=True)
def get_aa_opt_only_single_act(mdp_distr, state_abstr):
'''
Args:
mdp_distr
state_abstr (StateAbstraction)
Summary:
Computes an action abstraction where there exists an option that repeats a
single primitive action, for each primitive action that was optimal in
the ground state in the cluster.
'''
action_optimality_dict = state_abstr.get_act_opt_dict()
# Compute options.
options = []
for s_a in state_abstr.get_abs_states():
ground_states = state_abstr.get_ground_states_in_abs_state(s_a)
# One option per action.
for action in mdp_distr.get_actions():
list_of_state_with_a_optimal = []
for s_g in ground_states:
if action in action_optimality_dict[s_g]:
list_of_state_with_a_optimal.append(s_g)
if len(list_of_state_with_a_optimal) == 0:
continue
init_predicate = ContainsPredicate(
list_of_items=list_of_state_with_a_optimal)
term_predicate = NotPredicate(init_predicate)
policy_obj = Policy(action)
o = Option(init_predicate=init_predicate,
term_predicate=term_predicate,
policy=policy_obj.get_action,
term_prob=1 - mdp_distr.get_gamma())
options.append(o)
return ActionAbstraction(options=options,
prim_actions=mdp_distr.get_actions())
class AbstractionWrapper(Agent):
def __init__(self,
SubAgentClass,
actions,
state_abstr=None,
action_abstr=None,
name_ext="abstr"):
'''
Args:
SubAgentClass (simple_rl.AgentClass)
actions (list of str)
state_abstr (StateAbstraction)
state_abstr (ActionAbstraction)
name_ext (str)
'''
# Setup the abstracted agent.
self.agent = SubAgentClass(actions=actions)
self.action_abstr = ActionAbstraction(
options=self.agent.actions, prim_actions=self.agent.actions
) if action_abstr is None else action_abstr
self.state_abstr = StateAbstraction(
{}) if state_abstr is None else state_abstr
Agent.__init__(self,
name=self.agent.name + "-" + name_ext,
actions=self.action_abstr.get_actions())
def act(self, ground_state, reward):
'''
Args:
ground_state (State)
reward (float)
Return:
(str)
'''
abstr_state = self.state_abstr.phi(ground_state)
ground_action = self.action_abstr.act(self.agent, abstr_state,
ground_state, reward)
return ground_action
def reset(self):
# Write data.
self.agent.reset()
self.action_abstr.reset()
def end_of_episode(self):
self.agent.end_of_episode()
self.action_abstr.end_of_episode()
def get_aa_single_act(mdp_distr, state_abstr):
'''
Args:
mdp_distr
state_abstr (StateAbstraction)
Summary:
Computes an action abstraction where there exists an option that repeats a
single primitive action, for each primitive action that was optimal in the
cluster.
'''
action_optimality_dict = state_abstr.get_act_opt_dict()
options = []
# Compute options.
for s_a in state_abstr.get_abs_states():
init_predicate = EqPredicate(y=s_a, func=state_abstr.phi)
term_predicate = NeqPredicate(y=s_a, func=state_abstr.phi)
ground_states = state_abstr.get_ground_states_in_abs_state(s_a)
unique_a_star_in_cluster = set([])
for s_g in ground_states:
for a_star in action_optimality_dict[s_g]:
unique_a_star_in_cluster.add(a_star)
for action in unique_a_star_in_cluster:
policy_obj = Policy(action)
o = Option(init_predicate=init_predicate,
term_predicate=term_predicate,
policy=policy_obj.get_action)
options.append(o)
return ActionAbstraction(options=options,
prim_actions=mdp_distr.get_actions())
def add_layer_to_aa_stack(mdp_distr, sa_stack, aa_stack):
'''
Args:
mdp_distr (MDPDistribution)
sa_stack (StateAbstractionStack)
aa_stack (ActionAbstractionStack)
Returns:
(tuple):
1. (ActionAbstractionStack)
2. (MDPDistribution)
3. (bool)
'''
if aa_stack.get_num_levels() > 0:
abstr_mdp_distr = make_abstr_mdp.make_abstr_mdp_distr_multi_level(
mdp_distr, sa_stack, aa_stack)
else:
abstr_mdp_distr = mdp_distr
# Make options for the level + 1 height.
sa_stack.set_level_to_max()
next_options = aa_helpers.get_directed_options_for_sa(
abstr_mdp_distr,
sa_stack,
incl_self_loops=False,
max_options=1024 / (aa_stack.get_num_levels() + 1))
if not next_options:
# Too many options, decrease abstracton ratio and continue.
return aa_stack, True
next_aa = ActionAbstraction(options=next_options,
prim_actions=mdp_distr.get_actions())
aa_stack.add_aa(next_aa)
return aa_stack, False
class AbstractionWrapper(Agent):
def __init__(self,
SubAgentClass,
actions,
mdp_name,
max_option_steps=0,
state_abstr=None,
action_abstr=None,
name_ext="abstr"):
'''
Args:
SubAgentClass (simple_rl.AgentClass)
actions (list of str)
mdp_name (str)
state_abstr (StateAbstraction)
state_abstr (ActionAbstraction)
name_ext (str)
'''
# Setup the abstracted agent.
self._create_default_abstractions(actions, state_abstr, action_abstr)
self.agent = SubAgentClass(actions=self.action_abstr.get_actions())
self.exp_directory = os.path.join(os.getcwdu(), "results", mdp_name,
"options")
self.reward_since_tracking = 0
self.max_option_steps = max_option_steps
self.num_option_steps = 0
Agent.__init__(self,
name=self.agent.name + "-" + name_ext,
actions=self.action_abstr.get_actions())
self._setup_files()
def _setup_files(self):
'''
Summary:
Creates and removes relevant directories/files.
'''
if not os.path.exists(os.path.join(self.exp_directory)):
os.makedirs(self.exp_directory)
if os.path.exists(
os.path.join(self.exp_directory, str(self.name)) + ".csv"):
# Remove old
os.remove(
os.path.join(self.exp_directory, str(self.name)) + ".csv")
def write_datum_to_file(self, datum):
'''
Summary:
Writes datum to file.
'''
out_file = open(
os.path.join(self.exp_directory, str(self.name)) + ".csv", "a+")
out_file.write(str(datum) + ",")
out_file.close()
def _record_experience(self, ground_state, reward):
'''
Args:
abstr_state
abstr_action
reward
next_abstr_state
Summary:
Tracks experiences to display plots in terms of options.
'''
# if not self.action_abstr.is_next_step_continuing_option(ground_state):
self.write_datum_to_file(self.reward_since_tracking)
self.reward_since_tracking = 0
def _create_default_abstractions(self, actions, state_abstr, action_abstr):
'''
Summary:
We here create the default abstractions.
'''
if action_abstr is None:
self.action_abstr = ActionAbstraction(options=actions,
prim_actions=actions)
else:
self.action_abstr = action_abstr
self.state_abstr = StateAbstraction(
phi={}) if state_abstr is None else state_abstr
def act(self, ground_state, reward):
'''
Args:
ground_state (State)
reward (float)
Return:
(str)
'''
self.reward_since_tracking += reward
if self.max_option_steps > 0:
# We're counting action steps in terms of options.
if self.num_option_steps == self.max_option_steps:
# We're at the limit.
self._record_experience(ground_state, reward)
self.num_option_steps += 1
return "terminate"
elif self.num_option_steps > self.max_option_steps:
# Skip.
return "terminate"
elif not self.action_abstr.is_next_step_continuing_option(
ground_state):
# Taking a new option, count it and continue.
self.num_option_steps += 1
self._record_experience(ground_state, reward)
else:
self._record_experience(ground_state, reward)
abstr_state = self.state_abstr.phi(ground_state)
# print ground_state, abstr_state, hash(ground_state)
ground_action = self.action_abstr.act(self.agent, abstr_state,
ground_state, reward)
# print "ground_action", ground_action, type(ground_action), len(ground_action)
return ground_action
def reset(self):
# Write data.
out_file = open(
os.path.join(self.exp_directory, str(self.name)) + ".csv", "a+")
out_file.write("\n")
out_file.close()
self.agent.reset()
self.action_abstr.reset()
self.reward_since_tracking = 0
self.num_option_steps = 0
def new_task(self):
self._reset_reward()
def get_num_known_sa(self):
return self.agent.get_num_known_sa()
def _reset_reward(self):
if isinstance(self.agent, RMaxAgent):
self.agent._reset_reward()
def end_of_episode(self):
self.agent.end_of_episode()
self.action_abstr.end_of_episode()
