def __init__(self,
mutable_params: Union[dict, list],
abort_reward: int,
kernel: Kern,
gp_params: Dict[str, Any],
ctrls: List[Controller],
obs_template: Mapping[str, List[Union[List[str],
np.ndarray]]],
obs_varnames: List[str] = None,
**kwargs):
"""
Agent to execute safeopt algorithm (https://arxiv.org/abs/1509.01066) to control the environment by using
auxiliary controllers and Gaussian process to adopt the controller parameters (mutable_params) to safely
increase the performance.
:param mutable_params: safe inital controller parameters to adopt
:param abort_reward: factor to multiply with the initial reward to give back an abort_reward-times higher
negative reward in case of limit exceeded
:param kernel: kernel for the Gaussian process unsing GPy
:param gp_params: kernel parameters like bounds and lengthscale
:param ctrls: Controllers that are feed with the observations and exert actions on the environment
:param obs_template:
Template describing how the observation array should be transformed and passed to the internal controllers.
The key must match the name field of an internal controller.
The values are a list of:
- list of strings
- matching variable names of the state
- must match self.obs_varnames
- will be substituted by the values on runtime
- will be passed as an np.array of floats to the controller
- np.ndarray of floats (to be passed statically to the controller)
- a mixture of static and dynamic values in one parameter is not supported for performance reasons.
The values will be passed as parameters to the controllers step function.
:param obs_varnames: list of variable names that match the values of the observations
passed in the act function. Will be automatically set by the Runner class
"""
self.params = MutableParams(
list(mutable_params.values()) if isinstance(mutable_params, dict
) else mutable_params)
self.kernel = kernel
self.bounds = gp_params['bounds']
self.noise_var = gp_params['noise_var']
self.prior_mean = gp_params['prior_mean']
self.safe_threshold = gp_params['safe_threshold']
self.explore_threshold = gp_params['explore_threshold']
self.abort_reward = abort_reward
self.episode_reward = None
self.optimizer = None
self.inital_performance = None
self.last_best_performance = None
self.performance = None
self._iterations = 0
super().__init__(ctrls, obs_template, obs_varnames, **kwargs)
self.history.cols = ['J', 'Params']
def __init__(self,
mutable_params: Union[dict, list],
abort_reward: int,
kernel: Kern,
gp_params: Dict[str, Any],
ctrls: Dict[str, Controller],
observation_action_mapping: dict,
history=EmptyHistory()):
"""
Agent to execute safeopt algorithm (https://arxiv.org/abs/1509.01066) to control the environment by using
auxiliary controllers and Gaussian process to adopt the controller parameters (mutable_params) to safely
increase the performance.
:param mutable_params: safe inital controller parameters to adopt
:param abort_reward: factor to multiply with the initial reward to give back an abort_reward-times higher
negative reward in case of limit exceeded
:param kernel: kernel for the Gaussian process unsing GPy
:param gp_params: kernel parameters like bounds and lengthscale
:param ctrls: Controllers that are feed with the observations and exert actions on the environment
:param observation_action_mapping: form controller keys to observation keys, whose observation values will be
passed to the controller
:param history:Storage of internal data
"""
self.params = MutableParams(
list(mutable_params.values()) if isinstance(mutable_params, dict
) else mutable_params)
self.kernel = kernel
self.bounds = gp_params['bounds']
self.noise_var = gp_params['noise_var']
self.prior_mean = gp_params['prior_mean']
self.safe_threshold = gp_params['safe_threshold']
self.explore_threshold = gp_params['explore_threshold']
self.abort_reward = abort_reward
self.episode_reward = None
self.optimizer = None
self.inital_Performance = None
self._iterations = 0
super().__init__(ctrls, observation_action_mapping, history)
self.history.cols = ['J', 'Params']
class SafeOptAgent(StaticControlAgent):
def __init__(self,
mutable_params: Union[dict, list],
abort_reward: int,
kernel: Kern,
gp_params: Dict[str, Any],
ctrls: Dict[str, Controller],
observation_action_mapping: dict,
history=EmptyHistory()):
"""
Agent to execute safeopt algorithm (https://arxiv.org/abs/1509.01066) to control the environment by using
auxiliary controllers and Gaussian process to adopt the controller parameters (mutable_params) to safely
increase the performance.
:param mutable_params: safe inital controller parameters to adopt
:param abort_reward: factor to multiply with the initial reward to give back an abort_reward-times higher
negative reward in case of limit exceeded
:param kernel: kernel for the Gaussian process unsing GPy
:param gp_params: kernel parameters like bounds and lengthscale
:param ctrls: Controllers that are feed with the observations and exert actions on the environment
:param observation_action_mapping: form controller keys to observation keys, whose observation values will be
passed to the controller
:param history:Storage of internal data
"""
self.params = MutableParams(
list(mutable_params.values()) if isinstance(mutable_params, dict
) else mutable_params)
self.kernel = kernel
self.bounds = gp_params['bounds']
self.noise_var = gp_params['noise_var']
self.prior_mean = gp_params['prior_mean']
self.safe_threshold = gp_params['safe_threshold']
self.explore_threshold = gp_params['explore_threshold']
self.abort_reward = abort_reward
self.episode_reward = None
self.optimizer = None
self.inital_Performance = None
self._iterations = 0
super().__init__(ctrls, observation_action_mapping, history)
self.history.cols = ['J', 'Params']
def reset(self):
"""
Resets the kernel, episodic reward and the optimizer
"""
# reinstantiate kernel
kernel_params = self.kernel.to_dict()
cls_name = kernel_params['class']
mod = importlib.import_module('.'.join(cls_name.split('.')[:-1]))
cls = getattr(mod, cls_name.split('.')[-1])
remaining_params = {
k: v
for k, v in kernel_params.items() if k not in {'class', 'useGPU'}
}
self.kernel = cls(**remaining_params)
self.params.reset()
self.optimizer = None
self.episode_reward = 0
self.inital_Performance = None
self._iterations = 0
return super().reset()
def observe(self, reward, terminated):
"""
Makes an observation of the enviroment.
If terminated, then caclulates the performance and the next values for the parameters using safeopt
:param reward: reward of the simulation step
:param terminated: True if episode is over or aborted
:return:
"""
self._iterations += 1
self.episode_reward += reward or 0
if terminated:
# calculate MSE, divide Summed error by length of measurement
self.episode_reward = self.episode_reward / self._iterations
# safeopt update step
self.update_params()
# reset for new episode
self.prepare_episode()
# on other steps we don't need to do anything
def update_params(self):
"""
Sets up the Gaussian process in the first episodes, updates the parameters in the following.
"""
if self.optimizer is None:
# First Iteration
# self.inital_Performance = 1 / self.episode_reward
self.inital_Performance = self.episode_reward
# Norm for Safe-point
# J = 1 / self.episode_reward / self.inital_Performance
J = self.inital_Performance
# Define Mean "Offset": Like BK: Assume Mean = Threshold (BK = 0, now = 20% below first (safe) J: means: if
# new Performance is 20 % lower than the inital we assume as unsafe)
mf = GPy.core.Mapping(len(self.bounds), 1)
mf.f = lambda x: self.prior_mean * J
mf.update_gradients = lambda a, b: 0
mf.gradients_X = lambda a, b: 0
gp = GPy.models.GPRegression(np.array([self.params[:]]),
np.array([[J]]),
self.kernel,
noise_var=self.noise_var,
mean_function=mf)
self.optimizer = SafeOptSwarm(gp,
self.safe_threshold * J,
bounds=self.bounds,
threshold=self.explore_threshold * J)
else:
if np.isnan(self.episode_reward):
# set r to doubled (negative!) initial reward
self.episode_reward = self.abort_reward * self.inital_Performance
# toDo: set reward to -inf and stop agent?
# warning mit logger
logger.warning(
'UNSAFE! Limit exceeded, epsiode abort, give a reward of {} times the'
'initial reward'.format(self.abort_reward))
J = self.episode_reward
self.optimizer.add_new_data_point(self.params[:], J)
self.history.append([J, self.params[:]])
self.params[:] = self.optimizer.optimize()
def render(self):
"""
Renders the results for the performance
"""
plt.figure()
self.optimizer.plot(1000)
plt.show()
def prepare_episode(self):
"""
Prepares the next episode; reset iteration counting variable and call superclass to reset controllers
"""
self._iterations = 0
super().prepare_episode()
class SafeOptAgent(StaticControlAgent):
def __init__(self,
mutable_params: Union[dict, list],
abort_reward: int,
kernel: Kern,
gp_params: Dict[str, Any],
ctrls: List[Controller],
obs_template: Mapping[str, List[Union[List[str],
np.ndarray]]],
obs_varnames: List[str] = None,
**kwargs):
"""
Agent to execute safeopt algorithm (https://arxiv.org/abs/1509.01066) to control the environment by using
auxiliary controllers and Gaussian process to adopt the controller parameters (mutable_params) to safely
increase the performance.
:param mutable_params: safe inital controller parameters to adopt
:param abort_reward: factor to multiply with the initial reward to give back an abort_reward-times higher
negative reward in case of limit exceeded
:param kernel: kernel for the Gaussian process unsing GPy
:param gp_params: kernel parameters like bounds and lengthscale
:param ctrls: Controllers that are feed with the observations and exert actions on the environment
:param obs_template:
Template describing how the observation array should be transformed and passed to the internal controllers.
The key must match the name field of an internal controller.
The values are a list of:
- list of strings
- matching variable names of the state
- must match self.obs_varnames
- will be substituted by the values on runtime
- will be passed as an np.array of floats to the controller
- np.ndarray of floats (to be passed statically to the controller)
- a mixture of static and dynamic values in one parameter is not supported for performance reasons.
The values will be passed as parameters to the controllers step function.
:param obs_varnames: list of variable names that match the values of the observations
passed in the act function. Will be automatically set by the Runner class
"""
self.params = MutableParams(
list(mutable_params.values()) if isinstance(mutable_params, dict
) else mutable_params)
self.kernel = kernel
self.bounds = gp_params['bounds']
self.noise_var = gp_params['noise_var']
self.prior_mean = gp_params['prior_mean']
self.safe_threshold = gp_params['safe_threshold']
self.explore_threshold = gp_params['explore_threshold']
self.abort_reward = abort_reward
self.episode_reward = None
self.optimizer = None
self.inital_performance = None
self.last_best_performance = None
self.performance = None
self._iterations = 0
super().__init__(ctrls, obs_template, obs_varnames, **kwargs)
self.history.cols = ['J', 'Params']
def reset(self):
"""
Resets the kernel, episodic reward and the optimizer
"""
# reinstantiate kernel
kernel_params = self.kernel.to_dict()
cls_name = kernel_params['class']
mod = importlib.import_module('.'.join(cls_name.split('.')[:-1]))
cls = getattr(mod, cls_name.split('.')[-1])
remaining_params = {
k: v
for k, v in kernel_params.items() if k not in {'class', 'useGPU'}
}
self.kernel = cls(**remaining_params)
self.params.reset()
self.optimizer = None
self.episode_reward = 0
self.inital_performance = None
self.last_best_performance = None
self.performance = None
self._iterations = 0
return super().reset()
def observe(self, reward, terminated):
"""
Makes an observation of the enviroment.
If terminated, then caclulates the performance and the next values for the parameters using safeopt
:param reward: reward of the simulation step
:param terminated: True if episode is over or aborted
:return:
"""
self._iterations += 1
self.episode_reward += reward or 0
if terminated:
# calculate MSE, divide Summed error by length of measurement
self.performance = self.episode_reward / self._iterations
# safeopt update step
self.update_params()
# reset for new episode
self.prepare_episode()
# on other steps we don't need to do anything
def update_params(self):
"""
Sets up the Gaussian process in the first episodes, updates the parameters in the following.
"""
if self.optimizer is None:
# First Iteration
self.inital_performance = self.performance
# Norm for Safe-point
# J = 1 / self.episode_reward / self.inital_Performance
self.last_best_performance = self.performance
# Define Mean "Offset": Like BK: Assume Mean = Threshold (BK = 0, now = 20% below first (safe) J: means: if
# new Performance is 20 % lower than the inital we assume as unsafe)
mf = GPy.core.Mapping(len(self.bounds), 1)
mf.f = lambda x: self.prior_mean * self.performance
mf.update_gradients = lambda a, b: 0
mf.gradients_X = lambda a, b: 0
gp = GPy.models.GPRegression(
np.array([self.params[:]]), # noqa
np.array([[self.performance]]),
self.kernel,
noise_var=self.noise_var,
mean_function=mf)
self.optimizer = SafeOptSwarm(
gp,
self.safe_threshold * self.performance,
bounds=self.bounds,
threshold=self.explore_threshold * self.performance)
else:
if np.isnan(self.episode_reward):
# set r to doubled (negative!) initial reward
self.performance = self.abort_reward * self.inital_performance
# toDo: set reward to -inf and stop agent?
# warning mit logger
logger.warning(
'UNSAFE! Limit exceeded, epsiode abort, give a reward of {} times the'
'initial reward'.format(self.abort_reward))
self.optimizer.add_new_data_point(self.params[:], self.performance)
self.history.append([self.performance, self.params[:]])
self.params[:] = self.optimizer.optimize()
if self.has_improved:
# if performance has improved store the current last index of the df
self.best_episode = self.history.df.shape[0] - 1
self.last_best_performance = self.performance
def render(self) -> Figure:
"""
Renders the results for the performance
"""
figure, ax = plt.subplots()
if self.optimizer.x.size > 3:
# check if the dimensionality is less then 4 dimension
logger.info(
'Plotting of GP landscape not possible for then 3 dimensions')
return figure
self.optimizer.plot(1000, figure=figure)
# mark best performance in green
y, x = self.history.df.loc[self.best_episode, ['J', 'Params']]
if len(x) == 1:
ax.scatter([x], [y],
s=20 * 10,
marker='x',
linewidths=3,
color='g')
elif len(x) == 2:
ax.plot(x[0], x[1], 'og')
else:
logger.warning('Choose appropriate number of control parameters')
plt.show()
return figure
def prepare_episode(self):
"""
Prepares the next episode; reset iteration counting variable and call superclass to reset controllers
"""
self._iterations = 0
super().prepare_episode()
@property
def has_improved(self) -> bool:
"""
Defines if the performance increased or stays constant
:return: True, if performance was increased or equal, else False
"""
return self.performance >= self.last_best_performance
# For 1D example, if Ki should be adjusted
elif adjust == 'Ki':
mutable_params = dict(currentI=MutableFloat(5))
current_dqp_iparams = PI_params(kP=0.005, kI=mutable_params['currentI'], limits=(-1, 1))
# For 2D example, choose Kp and Ki as mutable parameters
elif adjust == 'Kpi':
mutable_params = dict(currentP=MutableFloat(0.04), currentI=MutableFloat(11.8))
current_dqp_iparams = PI_params(kP=mutable_params['currentP'], kI=mutable_params['currentI'],
limits=(-1, 1))
# Define a current sourcing inverter as master inverter using the pi and droop parameters from above
ctrl = MultiPhaseDQCurrentSourcingController(current_dqp_iparams, ts_sim=delta_t,
ts_ctrl=undersample * delta_t, name='master', f_nom=net.freq_nom)
i_ref = MutableParams([MutableFloat(f) for f in i_ref1])
#####################################
# Definition of the optimization agent
# The agent is using the SafeOpt algorithm by F. Berkenkamp (https://arxiv.org/abs/1509.01066) in this example
# Arguments described above
# History is used to store results
agent = SafeOptAgent(mutable_params,
abort_reward,
j_min,
kernel,
dict(bounds=bounds, noise_var=noise_var, prior_mean=prior_mean, safe_threshold=safe_threshold,
explore_threshold=explore_threshold), [ctrl],
dict(master=[[f'lc.inductor{k}.i' for k in '123'], i_ref]), history=FullHistory()
)
#####################################
def run_experiment(len_kp, len_ki):
if isfile(f'{save_folder}/{len_kp:.4f},{len_ki:.4f}.txt'):
with open(f'{save_folder}/{len_kp:.4f},{len_ki:.4f}.txt', 'r') as f:
return strtobool(f.read().strip())
rew = Reward(i_limit=iLimit,
i_nominal=iNominal,
mu_c=mu,
max_episode_steps=max_episode_steps,
obs_dict=[[f'lc.inductor{k}.i' for k in '123'],
'master.phase', [f'master.SPI{k}' for k in 'dq0']])
#####################################
# Definitions for the GP
prior_mean = 0 # 2 # mean factor of the GP prior mean which is multiplied with the first performance of the
# initial set
noise_var = 0.001 # 0.001 ** 2 # measurement noise sigma_omega
prior_var = 2 # prior variance of the GP
bounds = None
lengthscale = None
if adjust == 'Kp':
bounds = [(0.0001, 0.1)] # bounds on the input variable Kp
lengthscale = [
.025
] # length scale for the parameter variation [Kp] for the GP
# For 1D example, if Ki should be adjusted
if adjust == 'Ki':
bounds = [(0, 20)] # bounds on the input variable Ki
lengthscale = [
10
] # length scale for the parameter variation [Ki] for the GP
# For 2D example, choose Kp and Ki as mutable parameters (below) and define bounds and lengthscale for both of them
if adjust == 'Kpi':
bounds = [(0.001, 0.07), (2, 150)]
lengthscale = [0.012, 30.]
df_len = pd.DataFrame({
'lengthscale': lengthscale,
'bounds': bounds,
'balanced_load': balanced_load,
'barrier_param_mu': mu
})
# The performance should not drop below the safe threshold, which is defined by the factor safe_threshold times
# the initial performance: safe_threshold = 0.8 means. Performance measurement for optimization are seen as
# unsafe, if the new measured performance drops below 20 % of the initial performance of the initial safe (!)
# parameter set
safe_threshold = 0
j_min = cal_j_min(phase_shift, amp_dev) # Used for normalization
# The algorithm will not try to expand any points that are below this threshold. This makes the algorithm stop
# expanding points eventually.
# The following variable is multiplied with the first performance of the initial set by the factor below:
explore_threshold = 0
# Factor to multiply with the initial reward to give back an abort_reward-times higher negative reward in case of
# limit exceeded
# has to be negative due to normalized performance (regarding J_init = 1)
abort_reward = 100 * j_min
# Definition of the kernel
kernel = GPy.kern.Matern32(input_dim=len(bounds),
variance=prior_var,
lengthscale=lengthscale,
ARD=True)
#####################################
# Definition of the controllers
mutable_params = None
current_dqp_iparams = None
if adjust == 'Kp':
# mutable_params = parameter (Kp gain of the current controller of the inverter) to be optimized using
# the SafeOpt algorithm
mutable_params = dict(currentP=MutableFloat(0.04))
# Define the PI parameters for the current controller of the inverter
current_dqp_iparams = PI_params(kP=mutable_params['currentP'],
kI=12,
limits=(-1, 1))
# For 1D example, if Ki should be adjusted
elif adjust == 'Ki':
mutable_params = dict(currentI=MutableFloat(5))
current_dqp_iparams = PI_params(kP=0.005,
kI=mutable_params['currentI'],
limits=(-1, 1))
# For 2D example, choose Kp and Ki as mutable parameters
elif adjust == 'Kpi':
mutable_params = dict(currentP=MutableFloat(0.04),
currentI=MutableFloat(11.8))
current_dqp_iparams = PI_params(kP=mutable_params['currentP'],
kI=mutable_params['currentI'],
limits=(-1, 1))
# Define a current sourcing inverter as master inverter using the pi and droop parameters from above
ctrl = MultiPhaseDQCurrentSourcingController(current_dqp_iparams,
delta_t,
undersampling=undersample,
name='master',
f_nom=net.freq_nom)
i_ref = MutableParams([MutableFloat(f) for f in i_ref1])
#####################################
# Definition of the optimization agent
# The agent is using the SafeOpt algorithm by F. Berkenkamp (https://arxiv.org/abs/1509.01066) in this example
# Arguments described above
# History is used to store results
agent = SafeOptAgent(
mutable_params,
abort_reward,
j_min,
kernel,
dict(bounds=bounds,
noise_var=noise_var,
prior_mean=prior_mean,
safe_threshold=safe_threshold,
explore_threshold=explore_threshold),
[ctrl],
dict(master=[[f'lc.inductor{k}.i' for k in '123'], i_ref]),
history=FullHistory(),
)
#####################################
# Definition of the environment using a FMU created by OpenModelica
# (https://www.openmodelica.org/)
# Using an inverter supplying a load
# - using the reward function described above as callable in the env
# - viz_cols used to choose which measurement values should be displayed (here, only the 3 currents across the
# inductors of the inverters are plotted. Labels and grid is adjusted using the PlotTmpl (For more information,
# see UserGuide)
# - inputs to the models are the connection points to the inverters (see user guide for more details)
# - model outputs are the the 3 currents through the inductors and the 3 voltages across the capacitors
if include_simulate:
# Defining unbalanced loads sampling from Gaussian distribution with sdt = 0.2*mean
# r_load = Load(R, 0.1 * R, balanced=balanced_load, tolerance=0.1)
# l_load = Load(L, 0.1 * L, balanced=balanced_load, tolerance=0.1)
# i_noise = Noise([0, 0, 0], [0.0023, 0.0015, 0.0018], 0.0005, 0.32)
# if no noise should be included:
r_load = Load(R, 0 * R, balanced=balanced_load)
l_load = Load(L, 0 * L, balanced=balanced_load)
def reset_loads():
r_load.reset()
l_load.reset()
plotter = PlotManager(agent,
save_results=save_results,
save_folder=save_folder,
show_plots=show_plots)
def ugly_foo(t):
if t >= .05:
i_ref[:] = i_ref2
else:
i_ref[:] = i_ref1
return partial(l_load.give_value, n=2)(t)
env = gym.make(
'openmodelica_microgrid_gym:ModelicaEnv_test-v1',
# reward_fun=Reward().rew_fun,
reward_fun=rew.rew_fun_c,
# time_step=delta_t,
viz_cols=[
PlotTmpl([[f'lc.inductor{i}.i'
for i in '123'], [f'master.SPI{i}' for i in 'abc']],
callback=plotter.xylables_i_abc,
color=[['b', 'r', 'g'], ['b', 'r', 'g']],
style=[[None], ['--']]),
PlotTmpl([[f'master.m{i}' for i in 'abc']],
callback=lambda fig: plotter.update_axes(
fig,
title='Simulation',
ylabel='$m_{\mathrm{abc}}\,/\,\mathrm{}$')),
PlotTmpl([[f'master.CVI{i}'
for i in 'dq0'], [f'master.SPI{i}' for i in 'dq0']],
callback=plotter.xylables_i_dq0,
color=[['b', 'r', 'g'], ['b', 'r', 'g']],
style=[[None], ['--']])
],
log_level=logging.INFO,
viz_mode='episode',
max_episode_steps=max_episode_steps,
model_params={
'lc.resistor1.R': partial(r_load.give_value, n=0),
'lc.resistor2.R': partial(r_load.give_value, n=1),
'lc.resistor3.R': partial(r_load.give_value, n=2),
'lc.inductor1.L': partial(l_load.give_value, n=0),
'lc.inductor2.L': partial(l_load.give_value, n=1),
'lc.inductor3.L': ugly_foo
},
model_path='../../omg_grid/grid.paper.fmu',
# model_path='../omg_grid/omg_grid.Grids.Paper_SC.fmu',
net=net,
history=FullHistory(),
action_time_delay=1 * undersample)
runner = MonteCarloRunner(agent, env)
runner.run(num_episodes,
n_mc=n_MC,
visualise=True,
prepare_mc_experiment=reset_loads)
with open(f'{save_folder}/{len_kp:.4f},{len_ki:.4f}.txt', 'w') as f:
print(f'{agent.unsafe}', file=f)
return agent.unsafe