def _standardize_user_data(self, s, a, s_next, r, absorbing, theta,
check_batch_dim=False):
"""
Args:
s (numpy.array): the samples of the state (nsamples, state_dim)
a (numpy.array): the samples of the state (nsamples, action_dim)
s_next (numpy.array): the samples of the next (reached) state (nsamples, state_dim)
r (numpy.array): the sample of the reward (nsamples, )
theta (numpy.array): the sample of the Q-function parameters (1, n_params)
check_batch_dim (bool): default False
Returns:
The standardized values (s, a, s_next, r, theta)
"""
s = standardize_input_data(
s, ['s'],
[(None, self.state_dim)] if self.state_dim is not None else None,
exception_prefix='state')
a = standardize_input_data(
a, ['a'],
[(None, self.action_dim)] if self.action_dim is not None else None,
exception_prefix='action')
# r = standardize_input_data(r, ['r'], [(None, 1)],
# check_batch_dim=False, exception_prefix='reward')
s_next = standardize_input_data(
s_next, ['s_next'],
[(None, self.state_dim)] if self.state_dim is not None else None,
exception_prefix='state_next')
theta = standardize_input_data(theta, ['theta'],
(None, self.bellman_model.n_inputs()),
exception_prefix='theta')
check_array_lengths(s, a, s_next)
return s, a, s_next, r, absorbing, theta
def _standardize_user_data(self, s, a, s_next, r, check_batch_dim=False):
"""
Args:
s (numpy.array): the samples of the state (nsamples, state_dim)
a (numpy.array): the samples of the state (nsamples, action_dim)
s_next (numpy.array): the samples of the next (reached) state (nsamples, state_dim)
r (numpy.array): the sample of the reward (nsamples, )
check_batch_dim (bool): default False
Returns:
The standardized values (s, a, s_next, r, theta)
"""
s = standardize_input_data(s, ['s'], [(None, self.state_dim)] if self.state_dim is not None else None,
check_batch_dim=check_batch_dim, exception_prefix='state')
a = standardize_input_data(a, ['a'], [(None, self.action_dim)] if self.action_dim is not None else None,
check_batch_dim=check_batch_dim, exception_prefix='action')
# r = standardize_input_data(r, ['r'], [(None, 1)],
# check_batch_dim=False, exception_prefix='reward')
s_next = standardize_input_data(s_next, ['s_next'],
[(None, self.state_dim)] if self.state_dim is not None else None,
check_batch_dim=check_batch_dim, exception_prefix='state_next')
check_array_lengths(s, a, s_next)
return s, a, s_next, r
def _standardize_user_data(self, s, a, s_next, r, check_batch_dim=False):
"""
Args:
s (numpy.array): the samples of the state (nsamples, state_dim)
a (numpy.array): the samples of the state (nsamples, action_dim)
s_next (numpy.array): the samples of the next (reached) state (nsamples, state_dim)
r (numpy.array): the sample of the reward (nsamples, )
check_batch_dim (bool): default False
Returns:
The standardized values (s, a, s_next, r, theta)
"""
s = standardize_input_data(
s, ['s'],
[(None, self.state_dim)] if self.state_dim is not None else None,
check_batch_dim=check_batch_dim,
exception_prefix='state')
a = standardize_input_data(
a, ['a'],
[(None, self.action_dim)] if self.action_dim is not None else None,
check_batch_dim=check_batch_dim,
exception_prefix='action')
# r = standardize_input_data(r, ['r'], [(None, 1)],
# check_batch_dim=False, exception_prefix='reward')
s_next = standardize_input_data(
s_next, ['s_next'],
[(None, self.state_dim)] if self.state_dim is not None else None,
check_batch_dim=check_batch_dim,
exception_prefix='state_next')
check_array_lengths(s, a, s_next)
return s, a, s_next, r
def _standardize_user_data(self,
s,
a,
s_next,
r,
absorbing,
theta,
check_batch_dim=False):
"""
Args:
s (numpy.array): the samples of the state (nsamples, state_dim)
a (numpy.array): the samples of the state (nsamples, action_dim)
s_next (numpy.array): the samples of the next (reached) state (nsamples, state_dim)
r (numpy.array): the sample of the reward (nsamples, )
theta (numpy.array): the sample of the Q-function parameters (1, n_params)
check_batch_dim (bool): default False
Returns:
The standardized values (s, a, s_next, r, theta)
"""
s = standardize_input_data(
s, ['s'],
[(None, self.state_dim)] if self.state_dim is not None else None,
exception_prefix='state')
a = standardize_input_data(
a, ['a'],
[(None, self.action_dim)] if self.action_dim is not None else None,
exception_prefix='action')
# r = standardize_input_data(r, ['r'], [(None, 1)],
# check_batch_dim=False, exception_prefix='reward')
s_next = standardize_input_data(
s_next, ['s_next'],
[(None, self.state_dim)] if self.state_dim is not None else None,
exception_prefix='state_next')
theta = standardize_input_data(theta, ['theta'],
(None, self.bellman_model.n_inputs()),
exception_prefix='theta')
check_array_lengths(s, a, s_next)
return s, a, s_next, r, absorbing, theta
