def get_skillset(self):
# print(self.skill_set)
# print(self.actions)
to_return = DictTree({
skill: DictTree(
step=None,
model_name=getattr(skill_class, 'model_name', "log_poly2"),
arg_in_len=skill_class.arg_in_len,
max_cnt=getattr(skill_class, 'max_cnt', None),
sub_skill_names=getattr(skill_class, 'sub_skill_names', []),
ret_out_len=skill_class.ret_out_len,
min_valid_data=getattr(skill_class, 'min_valid_data', None),
sub_arg_accuracy=getattr(skill_class, 'sub_arg_accuracy',
None),
)
for skill, skill_class in list(self.skill_set.items()) +
list(self.actions.items())
})
for skill in to_return.values():
if skill.sub_skill_names:
skill.ret_in_len = max(
to_return[sub_skill_name].ret_out_len
for sub_skill_name in skill.sub_skill_names)
skill.arg_out_len = max(
skill.ret_out_len,
max(to_return[sub_skill_name].arg_in_len
for sub_skill_name in skill.sub_skill_names))
self.stack = None
self.last_act_name = None
return to_return
class HierarchicalAgent(agent.Agent):
def __init__(self, config):
super(HierarchicalAgent, self).__init__(config)
self.skillset = DictTree({
skill.__name__: DictTree(
step=getattr(skill, 'step', None) if config.rollable else None,
model_name=getattr(skill, 'model_name',
self.default_model_name),
arg_in_len=skill.arg_in_len,
max_cnt=getattr(skill, 'max_cnt', None),
sub_skill_names=getattr(skill, 'sub_skill_names', []),
ret_out_len=skill.ret_out_len,
min_valid_data=getattr(skill, 'min_valid_data', None),
sub_arg_accuracy=getattr(skill, 'sub_arg_accuracy', None),
)
for skill in self.skills + self.actions
})
for skill in self.skillset.values():
if skill.sub_skill_names:
skill.ret_in_len = max(
self.skillset[sub_skill_name].ret_out_len
for sub_skill_name in skill.sub_skill_names)
skill.arg_out_len = max(
skill.ret_out_len,
max(self.skillset[sub_skill_name].arg_in_len
for sub_skill_name in skill.sub_skill_names))
if config.rollable and not config.teacher:
for skill_name, skill in self.skillset.items():
if skill.sub_skill_names:
skill.step = load_skill(config.model_dirname, skill_name,
skill)
self.stack = None
self.last_act_name = None
@property
def root_skill_name(self):
raise NotImplementedError
@property
def skills(self):
raise NotImplementedError
@property
def actions(self):
raise NotImplementedError
@property
def default_model_name(self):
raise NotImplementedError
def reset(self, init_arg):
self.stack = [DictTree(name=self.root_skill_name, arg=init_arg, cnt=0)]
self.last_act_name = None
def step(self, obs):
ret_name = self.last_act_name
ret_val = obs
steps = []
while self.stack:
top = self.stack[-1]
sub_name, sub_arg = self.skillset[top.name].step(
top.arg, top.cnt, ret_name, ret_val, obs)
steps.append(
DictTree(
name=top.name,
arg=top.arg,
cnt=top.cnt,
ret_name=ret_name,
ret_val=ret_val,
sub_name=sub_name,
sub_arg=sub_arg,
))
print("{}({}, {}, {}, {}) -> {}({})".format(
top.name, top.arg, top.cnt, ret_name, ret_val, sub_name,
sub_arg))
if sub_name is None:
self.stack.pop()
ret_name = top.name
ret_val = sub_arg
elif self.skillset[sub_name.replace('Record_',
'')].sub_skill_names:
top.cnt += 1
self.stack.append(DictTree(name=sub_name, arg=sub_arg, cnt=0))
ret_name = None
ret_val = None
else:
top.cnt += 1
self.last_act_name = sub_name
return sub_name, sub_arg, DictTree(steps=steps)
self.last_act_name = None
return None, None, DictTree(steps=steps)