def step(self, action):
"""
This function performs one simulation step in a RFL algorithm.
It updates the state and returns a reward according to the chosen reward-function.
This is not affected by the noise.
"""
next_t = self.t + self.dt
self._adjust_parameters(action)
self.state = self._perform_step(next_t)
self.t = next_t
if self._arrived_at_final_state():
self.final_state = True
reward = self.reward_function(action)
if not self._inside_planetary_boundaries():
self.final_state = True
trafo_state = compactification(self.state, self.current_state)
part_state = trafo_state[self.obs_array]
return_state = self._add_noise(part_state)
# print("Step Noisy Environment", return_state)
return return_state, reward, self.final_state
def step(self, action):
"""
This function performs one simulation step in a RFL algorithm.
It updates the state and returns a reward according to the chosen reward-function.
"""
next_t= self.t + self.dt
self._adjust_parameters(action)
self.state=self._perform_step( next_t)
self.t=next_t
if self._arrived_at_final_state():
self.final_state = True
reward=self.reward_function(action)
if not self._inside_planetary_boundaries():
self.final_state = True
measured_states=self._get_measurement_PB(self.state)
trafo_state=compactification(measured_states, self.ini_measured_state )
return_state=self._add_noise(trafo_state)
# print("Step PB", return_state)
return return_state, reward, self.final_state
def reset(self):
self.start_state = self.state = np.array(
self.current_state_region_StartPoint())
trafo_state = compactification(self.state, self.current_state)
self.final_state = False
self.t = self.t0
return_state = trafo_state[self.obs_array]
return return_state
def reset(self):
self.start_state=self.state=np.array(self.current_state_region_StartPoint())
measured_states=self._get_measurement_PB(self.state)
trafo_state=compactification(measured_states, self.ini_measured_state)
self.final_state=False
self.t=self.t0
return_state=self._add_noise(trafo_state)
return return_state
def reset_for_state(self, state=None):
if state == None:
self.start_state = self.state = self.current_state
else:
self.start_state = self.state = np.array(state)
self.final_state = False
self.t = self.t0
trafo_state = compactification(self.state, self.current_state)
return_state = trafo_state[self.obs_array]
# print("Reset to state: " , return_state)
return return_state
def reset_for_state(self, state=None):
if state==None:
self.start_state=self.state=self.current_state
else:
self.start_state=self.state=np.array(state)
self.final_state=False
self.t=self.t0
measured_states=self._get_measurement_PB(self.state)
trafo_state=compactification(measured_states, self.ini_measured_state)
return_state=self._add_noise(trafo_state)
# print("Reset to state: " , return_state)
return return_state