def __init__(self):
variables = {
'dynamics': None, # Dynamics object for the current iteration.
'x0mu': None, # Mean for the initial state, used by the dynamics.
'x0sigma': None, # Covariance for the initial state distribution.
'cc': None, # Cost estimate constant term.
'cv': None, # Cost estimate vector term.
'Cm': None, # Cost estimate matrix term.
'last_kl_step': float('inf'), # KL step of the previous iteration.
}
BundleType.__init__(self, variables)
def __init__(self):
variables = {
'dynamics': None, # Dynamics object for the current iteration.
'x0mu': None, # Mean for the initial state, used by the dynamics.
'x0sigma': None, # Covariance for the initial state distribution.
'cc': None, # Cost estimate constant term.
'cv': None, # Cost estimate vector term.
'Cm': None, # Cost estimate matrix term.
'last_kl_step': float('inf'), # KL step of the previous iteration.
}
BundleType.__init__(self, variables)
def __init__(self):
variables = {
'sample_list': None, # List of samples for the current iteration.
'traj_info': None, # Current TrajectoryInfo object.
'pol_info': None, # Current PolicyInfo object.
'traj_distr': None, # Initial trajectory distribution.
'cs': None, # Sample costs of the current iteration.
'step_mult': 1.0, # KL step multiplier for the current iteration.
'eta': 1.0, # Dual variable used in LQR backward pass.
}
BundleType.__init__(self, variables)
def __init__(self):
variables = {
'sample_list': None, # List of samples for the current iteration.
'traj_info': None, # Current TrajectoryInfo object.
'pol_info': None, # Current PolicyInfo object.
'traj_distr': None, # Initial trajectory distribution.
'new_traj_distr': None, # Updated trajectory distribution.
'cs': None, # Sample costs of the current iteration.
'step_mult': 1.0, # KL step multiplier for the current iteration.
'eta': 1.0, # Dual variable used in LQR backward pass.
}
BundleType.__init__(self, variables)
def __init__(self, hyperparams):
T, dU, dX = hyperparams['T'], hyperparams['dU'], hyperparams['dX']
variables = {
'pol_mu': None, # Mean of the current policy output.
'pol_sig': None, # Covariance of the current policy output.
'pol_K': np.zeros((T, dU, dX)), # Policy linearization.
'pol_k': np.zeros((T, dU)), # Policy linearization.
'pol_S': np.zeros((T, dU, dU)), # Policy linearization covariance.
'chol_pol_S': np.zeros((T, dU, dU)), # Cholesky decomp of covar.
'policy_prior': None, # Current prior for policy linearization.
}
BundleType.__init__(self, variables)
def __init__(self):
variables = {
'sample_list': None, # List of samples for the current iteration.
'syn_sample_list': None, # List of synthetic samples
'traj_info': None, # Current TrajectoryInfo object.
'prevcost_traj_info': None, # Current TrajectoryInfo object using previous IOC cost.
'init_pol_info': None, # Initial PolicyInfo object
'pol_info': None, # Current PolicyInfo object.
'traj_distr': None, # Initial trajectory distribution.
'cs': None, # Sample costs of the current iteration.
'cgt': None, # Ground truth sample cost of the current iteration.
'step_mult': 1.0, # KL step multiplier for the current iteration.
'eta': 1.0, # Dual variable used in LQR backward pass.
}
BundleType.__init__(self, variables)
def __init__(self):
variables = {
'dynamics': None, # Dynamics object for the current iteration.
'x0mu': None, # Mean for the initial state, used by the dynamics.
'x0sigma': None, # Covariance for the initial state distribution.
'xmu': None, # Mean of real world trajectory distribution
'ref_x': None, # Reference states
'ref_u': None, # Reference actions
'xmusigma': None, # Covariance of real world trajectory distribution
'cc': None, # Cost estimate constant term.
'cv': None, # Cost estimate vector term.
'Cm': None, # Cost estimate matrix term.
'cs': None, # Actual costs
'last_kl_step': float('inf'), # KL step of the previous iteration.
}
BundleType.__init__(self, variables)
def __init__(self, hyperparams):
T, dU, dX = hyperparams['T'], hyperparams['dU'], hyperparams['dX']
variables = {
'lambda_k': np.zeros((T, dU)), # Dual variables.
'lambda_K': np.zeros((T, dU, dX)), # Dual variables.
'pol_wt': hyperparams['init_pol_wt'] * np.ones(T), # Policy weight.
'pol_mu': None, # Mean of the current policy output.
'pol_sig': None, # Covariance of the current policy output.
'pol_K': np.zeros((T, dU, dX)), # Policy linearization.
'pol_k': np.zeros((T, dU)), # Policy linearization.
'pol_S': np.zeros((T, dU, dU)), # Policy linearization covariance.
'chol_pol_S': np.zeros((T, dU, dU)), # Cholesky decomp of covar.
'prev_kl': None, # Previous KL divergence.
'policy_samples': [], # List of current policy samples.
'policy_prior': None, # Current prior for policy linearization.
}
BundleType.__init__(self, variables)
def __init__(self, hyperparams):
T, dU, dX = hyperparams['T'], hyperparams['dU'], hyperparams['dX']
variables = {
'lambda_k': np.zeros((T, dU)), # Dual variables.
'lambda_K': np.zeros((T, dU, dX)), # Dual variables.
'pol_wt': hyperparams['init_pol_wt'] * np.ones(T), # Policy weight.
'pol_mu': None, # Mean of the current policy output.
'pol_sig': None, # Covariance of the current policy output.
'pol_K': np.zeros((T, dU, dX)), # Policy linearization.
'pol_k': np.zeros((T, dU)), # Policy linearization.
'pol_S': np.zeros((T, dU, dU)), # Policy linearization covariance.
'chol_pol_S': np.zeros((T, dU, dU)), # Cholesky decomp of covar.
'prev_kl': None, # Previous KL divergence.
'init_kl': None, # The initial KL divergence, before the iteration.
'policy_samples': [], # List of current policy samples.
'policy_prior': None, # Current prior for policy linearization.
}
BundleType.__init__(self, variables)