def step(self, action):
return_ro = Reward_observation_terminal()
return_ro.r = self._calculate_reward(action)
return_ro.o = Observation()
return_ro.terminal = self._check_terminal(action)
return return_ro
def step(self, action):
return_ro = Reward_observation_terminal()
return_ro.r = -1.0
return_ro.o = Observation()
if len(action.doubleArray) == 0:
return_ro.terminal = True
else:
return_ro.terminal = False
return return_ro
def step(self, action):
# Make sure the action is valid
assert len(action.intArray) == 1, "Expected 1 integer action."
assert action.intArray[0] >= 0, "Expected action to be in [0,3]"
assert action.intArray[0] < 4, "Expected action to be in [0,3]"
self.update_position(action.intArray[0])
obs = Observation()
obs.intArray = [self.calculate_flat_state()]
return_ro = Reward_observation_terminal()
return_ro.r = self.calculate_reward()
return_ro.o = obs
return_ro.terminal = self.check_current_terminal()
return return_ro
def step(self, action):
return_ro = Reward_observation_terminal()
self._apply(action)
self._render(self._sensors[0])
return_ro.terminal = self._is_terminal()
return_ro.r = -1.
if return_ro.terminal:
return_ro.r = .0
if self._reward_noise > 0:
return_ro.r += np.random.normal(scale=self._reward_noise)
obs = Observation()
obs.doubleArray = self._sensors.tolist()
return_ro.o = obs
return return_ro
