def get_state_space(self):
if self.processor is None:
return Space.convert_openai_space(self.env.observation_space)
else:
self.reset()
shape = self.current_state.shape
min = np.zeros(shape, dtype=np.uint8)
max = np.full(shape, 255, dtype=np.uint8)
return Space(min, max, True)
def get_state_space(self):
if self.__processor is None:
shape = self.__current_buffer.shape
else:
shape = self.__processor.process(self.__current_buffer).shape
min_value = np.zeros(shape, dtype=np.uint8)
max_value = np.full(shape, 255)
return Space(min_value, max_value, True)
def get_state_space(self):
from fruit.types.priv import Space
state_spec = self.env.observation_spec()
for d in state_spec:
if d['name'] == self.mode:
shape = d['shape']
return Space(np.full(shape, 0), np.full(shape, 255), True)
return None
def get_state_space(self):
from fruit.types.priv import Space
if self.processor is None:
return self.game.get_state_space()
else:
min = np.zeros([84, 84], dtype=np.uint8)
max = np.full([84, 84], 255)
return Space(min, max, True)
def get_action_space(self, act_space=None):
from fruit.types.priv import Space
if act_space is None:
act_space = self.environment.actions()
print(act_space)
if 'num_values' in act_space:
return Space(0, act_space['num_values'] - 1, True)
elif 'num_actions' in act_space:
return Space(0, act_space['num_actions'] - 1, True)
elif 'shape' in act_space:
if 'min_value' in act_space and 'max_value' in act_space:
min_value = act_space['min_value']
max_value = act_space['max_value']
if np.asarray(
act_space['min_value']).shape != act_space['shape']:
min_value = np.full(act_space['shape'],
act_space['min_value'])
max_value = np.full(act_space['shape'],
act_space['max_value'])
elif 'type' in act_space:
if isinstance(act_space['type'],
bool) or act_space['type'] == 'bool':
min_value = np.full(act_space['shape'], 0)
max_value = np.full(act_space['shape'], 1)
return Space(min_value, max_value, True)
else:
min_value = np.full(act_space['shape'], 0)
max_value = np.full(act_space['shape'], 1)
else:
min_value = np.full(act_space['shape'], 0)
max_value = np.full(act_space['shape'], 1)
return Space(min_value, max_value, False)
elif 'gymtpl0' in act_space:
values = []
for i in range(len(act_space)):
key = 'gymtpl' + str(i)
values.append(self.get_action_space(act_space[key]))
return tuple(values)
else:
raise ValueError(
'Action space {} is not supported !'.format(act_space))
def get_action_space(self):
from fruit.types.priv import Space
return tuple([Space(0.0, 1.0, False),
Space(-1.0, 1.0, False),
Space(0.0, 1.0, False),
Space(0, 1, True),
Space(0, 1, True),
Space(0, 1, True),
Space(0, 2, True)])
def get_state_space(self, st_space=None):
from fruit.types.priv import Space
if st_space is None:
st_space = self.environment.states()
print(st_space)
if 'num_values' in st_space:
return Space(0, st_space['num_values'] - 1, True)
elif 'shape' in st_space:
if self.processor is None:
shape = st_space['shape']
else:
shape = self.current_state.shape
min_value = np.zeros(shape)
max_value = np.full(shape, 1.)
return Space(min_value, max_value, True)
elif 'gymtpl0' in st_space:
values = []
for i in range(len(st_space)):
key = 'gymtpl' + str(i)
values.append(self.get_state_space(st_space[key]))
return tuple(values)
else:
raise ValueError('State space {} is not supported !'.format(st_space))
def get_action_space(self):
if self.__action_reduction >= 1:
return Space(0, self.__action_reduction - 1, True)
else:
return Space(0, len(self.__action_set) - 1, True)
def get_action_space(self):
return Space.convert_openai_space(self.env.action_space)
def get_action_space(self):
from fruit.types.priv import Space
return Space(0, len(self.game.get_action_space()) - 1, True)
def get_state_space(self):
from fruit.types.priv import Space
shape = (20, 1)
min_value = np.zeros(shape)
max_value = np.full(shape, 100)
return Space(min_value, max_value, True)
def get_action_space(self):
from fruit.types.priv import Space
action_spec = self.env.action_spec()
min_values = [d['min'] for d in action_spec]
max_values = [d['max'] for d in action_spec]
return Space(min_values, max_values, True)
def get_state_space(self):
from fruit.types.priv import Space
return Space(0, 3**(self.size * self.size), True)
def get_state_space(self):
if self.graphical_state:
return [self.screen_width, self.screen_height]
else:
from fruit.types.priv import Space
return Space(0, self.num_of_rows * self.num_of_columns - 1, True)
def get_action_space(self):
from fruit.types.priv import Space
return Space(0, 5, True)
def get_state_space(self):
from fruit.types.priv import Space
shape = (self.width, self.height, 3)
min_value = np.zeros(shape)
max_value = np.full(shape, 255)
return Space(min_value, max_value, True)
def get_state_space(self):
if self.graphical_state:
return [self.screen_size, self.screen_size]
else:
from fruit.types.priv import Space
return Space(0, self.max_states_per_dim * self.max_states_per_dim - 1, True)
