def _advance_iteration_variables(self):
"""Move all 'cur' variables to 'prev', reinitialize 'cur' variables, and advance iteration counter."""
Algorithm._advance_iteration_variables(self)
for m in range(self.M):
self.cur[m].traj_info.last_kl_step = self.prev[
m].traj_info.last_kl_step
self.cur[m].pol_info = copy.deepcopy(self.prev[m].pol_info)
def _advance_iteration_variables(self):
"""
Move all 'cur' variables to 'prev', reinitialize 'cur'
variables, and advance iteration counter.
"""
Algorithm._advance_iteration_variables(self)
for m in range(self.M):
self.cur[m].traj_info.last_kl_step = \
self.prev[m].traj_info.last_kl_step
self.cur[m].pol_info = copy.deepcopy(self.prev[m].pol_info)
def __init__(self, hyperparams):
# ALG_BADMM = {
# 'inner_iterations': 4,
# 'policy_dual_rate': 0.1,
# 'policy_dual_rate_covar': 0.0,
# 'fixed_lg_step': 0,
# 'lg_step_schedule': 10.0,
# 'ent_reg_schedule': 0.0,
# 'init_pol_wt': 0.01,
# 'policy_sample_mode': 'add',
# 'exp_step_increase': 2.0,
# 'exp_step_decrease': 0.5,
# 'exp_step_upper': 0.5,
# 'exp_step_lower': 1.0,
# }
# Copy and update parameters
config = copy.deepcopy(ALG_BADMM)
# update() adds dictionary dict2's key-values pairs in to dict
config.update(hyperparams)
# Initialize (algorithm.py)
Algorithm.__init__(self, config)
# algorithm['policy_prior'] = {
# 'type': PolicyPriorGMM,
# 'max_clusters': 20,
# 'min_samples_per_cluster': 40,
# 'max_samples': 40,
# }
policy_prior = self._hyperparams['policy_prior']
# self._cond_idx = hyperparams['train_conditions']
# self.M = hyperparams['conditions'] = 2
for m in range(self.M):
# self.cur = [IterationData() for _ in range(self.M)]
# Initialize policy information
self.cur[m].pol_info = PolicyInfo(self._hyperparams)
self.cur[m].pol_info.policy_prior = \
policy_prior['type'](policy_prior)
# algorithm['policy_opt'] = {
# 'type': PolicyOptTf,
# 'network_params': {
# 'obs_include': [JOINT_ANGLES, JOINT_VELOCITIES],
# 'obs_vector_data': [JOINT_ANGLES, JOINT_VELOCITIES],
# 'sensor_dims': SENSOR_DIMS,
# },
# 'network_model': tf_network,
# 'iterations': 1000,
# 'weights_file_prefix': EXP_DIR + 'policy',
# }
self.policy_opt = self._hyperparams['policy_opt']['type'](
self._hyperparams['policy_opt'], self.dO, self.dU)
def __init__(self, hyperparams):
config = copy.deepcopy(ALG_BADMM)
config.update(hyperparams)
Algorithm.__init__(self, config)
policy_prior = self._hyperparams['policy_prior']
for m in range(self.M):
self.cur[m].pol_info = PolicyInfo(self._hyperparams)
self.cur[m].pol_info.policy_prior = \
policy_prior['type'](policy_prior)
self.policy_opt = self._hyperparams['policy_opt']['type'](
self._hyperparams['policy_opt'], self.dO, self.dU)
def __init__(self, hyperparams):
config = copy.deepcopy(ALG_MDGPS)
config.update(hyperparams)
Algorithm.__init__(self, config)
for m in range(self.M):
self.cur[m].pol_info = PolicyInfo(self._hyperparams)
policy_prior = self._hyperparams['policy_prior']
self.cur[m].pol_info.policy_prior = \
policy_prior['type'](policy_prior)
self.policy_opt = self._hyperparams['policy_opt']['type'](
self._hyperparams['policy_opt'], self.dO, self.dU
)
def __init__(self, hyperparams):
config = copy.deepcopy(ALG_OLGPS)
config.update(hyperparams)
Algorithm.__init__(self, config)
self.policy_opt = self._hyperparams['policy_opt']['type'](
self._hyperparams['policy_opt'], self.dO, self.dU
)
self.flag_reset = False
policy_prior = self._hyperparams['policy_prior']
for m in range(self.M):
self.cur[m].last_pol = PolicyInfo(self._hyperparams)
self.cur[m].last_pol.policy_prior = \
policy_prior['type'](policy_prior)
def __init__(self, hyperparams):
config = copy.deepcopy(ALG_PI2)
config.update(hyperparams)
Algorithm.__init__(self, config)
def __init__(self, hyperparams):
Algorithm.__init__(self, hyperparams)
def __init__(self, hyperparams):
Algorithm.__init__(self, hyperparams)
self.policy_opt = self._hyperparams['policy_opt']['type'](
self._hyperparams['policy_opt'], self.dO, self.dU)
