def predict(self,
state,
deterministic_=True,
evaluation_=False,
p=None,
sim_index=None,
bootstrapping=False):
# states = np.zeros((self._batch_size, self._state_length), dtype=self._settings['float_type'])
# states[0, ...] = state
# state = np.array(state, dtype=self._settings['float_type'])
state = norm_state(state, self._state_bounds)
state = np.array(state, dtype=self._settings['float_type'])
self._model.setStates(state)
# action_ = lasagne.layers.get_output(self._model.getActorNetwork(), state, deterministic=deterministic_).mean()
# action_ = scale_action(self._q_action()[0], self._action_bounds)
# if deterministic_:
action_ = scale_action(
self._model.getActorNetwork().predict(
state, batch_size=1)[:, :self._action_length],
self._action_bounds)
# action_ = scale_action(self._q_action_target()[0], self._action_bounds)
# else:
# action_ = scale_action(self._q_action()[0], self._action_bounds)
# action_ = q_valsActA[0]
return action_
def predict(self,
state,
deterministic_=True,
evaluation_=False,
p=None,
sim_index=None,
bootstrapping=False):
# states = np.zeros((self._batch_size, self._state_length), dtype=self._settings['float_type'])
# states[0, ...] = state
"""
if ( ('disable_parameter_scaling' in self._settings) and (self._settings['disable_parameter_scaling'])):
pass
else:
"""
# print ("Agent state bounds: ", self._state_bounds)
state = norm_state(state, self._state_bounds)
# print ("Agent normalized state: ", state)
state = np.array(state, dtype=self._settings['float_type'])
self._model.setStates(state)
# action_ = lasagne.layers.get_output(self._model.getActorNetwork(), state, deterministic=deterministic_).mean()
# action_ = scale_action(self._q_action()[0], self._action_bounds)
# if deterministic_:
if (('disable_parameter_scaling' in self._settings)
and (self._settings['disable_parameter_scaling'])):
action_ = self._q_action()
# action_ = scale_action(self._q_action()[0], self._action_bounds)
else:
action_ = scale_action(self._q_action(), self._action_bounds)
# print ("Agent Scaled action: ", action_)
# action_ = scale_action(self._q_action_target()[0], self._action_bounds)
# else:
# action_ = scale_action(self._q_action()[0], self._action_bounds)
# action_ = q_valsActA[0]
return action_
def predictWithDropout(self, state, deterministic_=True):
# states = np.zeros((self._batch_size, self._state_length), dtype=self._settings['float_type'])
# states[0, ...] = state
state = np.array(state, dtype=self._settings['float_type'])
state = norm_state(state, self._state_bounds)
action_ = scale_action(
self._model.getActorNetwork().predict(
states, batch_size=1)[:, :self._action_length],
self._action_bounds)
# else:
# action_ = scale_action(self._q_action()[0], self._action_bounds)
# action_ = q_valsActA[0]
return action_
def predict(self,
state,
deterministic_=True,
evaluation_=False,
p=None,
sim_index=None,
bootstrapping=False):
state = norm_state(state, self._state_bounds)
state = np.array(state, dtype=self._settings['float_type'])
action_ = scale_action(
self._model.getActorNetwork().predict(
state, batch_size=1)[:, :self._action_length],
self._action_bounds)
return action_
def predict(self,
state,
deterministic_=True,
evaluation_=False,
p=None,
sim_index=None,
bootstrapping=False):
state = norm_state(state, self._state_bounds)
state = np.array(state, dtype=self._settings['float_type'])
self._model.setStates(state)
if (('disable_parameter_scaling' in self._settings)
and (self._settings['disable_parameter_scaling'])):
action_ = self._q_action()[0]
else:
action_ = scale_action(
self._q_action()[0],
self._action_bounds) # transform the action value to a range
return action_
def predictWithDropout(self, state, deterministic_=True):
# states = np.zeros((self._batch_size, self._state_length), dtype=self._settings['float_type'])
# states[0, ...] = state
"""
if ( ('disable_parameter_scaling' in self._settings) and (self._settings['disable_parameter_scaling'])):
pass
else:
"""
state = np.array(state, dtype=self._settings['float_type'])
state = norm_state(state, self._state_bounds)
self._model.setStates(state)
# action_ = lasagne.layers.get_output(self._model.getActorNetwork(), state, deterministic=deterministic_).mean()
# action_ = scale_action(self._q_action()[0], self._action_bounds)
# if deterministic_:
action_ = scale_action(self._q_action_drop(), self._action_bounds)
# else:
# action_ = scale_action(self._q_action()[0], self._action_bounds)
# action_ = q_valsActA[0]
return action_
def predictWithDropout(self, state, deterministic_=True):
state = np.array(state, dtype=self._settings['float_type'])
state = norm_state(state, self._state_bounds)
self._model.setStates(state)
action_ = scale_action(self._q_action_drop()[0], self._action_bounds)
return action_