class PG_concurrent(BatchPolopt):
"""
Designed to enable concurrent training of a SNN that parameterizes skills
and also train the manager at the same time
Note that, if I'm not trying to do the sample approximation of the weird log of sum term,
I don't need to know which skill was picked, just need to know the action
"""
# double check this constructor later
def __init__(
self,
manager_optimizer=None,
optimizer=None,
snn_optimizer=None,
optimizer_args=None,
step_size=1e-6,
latents=None, # some sort of iterable of the actual latent vectors
period=10, # how often I choose a latent
truncate_local_is_ratio=None,
**kwargs):
if optimizer is None:
if optimizer_args is None:
# optimizer_args = dict()
optimizer_args = dict(batch_size=None)
self.optimizer = FirstOrderOptimizer(
learning_rate=step_size,
**optimizer_args) # I hope this is right
self.manager_optimizer = manager_optimizer
self.snn_optimizer = snn_optimizer
self.step_size = step_size
self.truncate_local_is_ratio = truncate_local_is_ratio
super(PG_concurrent,
self).__init__(**kwargs) # not sure if this line is correct
self.latents = latents
self.period = period
# todo: fix this sampler stuff
self.sampler = HierBatchSampler(self, self.period)
# i hope this is right
self.diagonal = DiagonalGaussian(
self.policy.low_policy.action_space.flat_dim)
self.debug_fns = []
# initialize the computation graph
# optimize is run on >= 1 trajectory at a time
def init_opt(self):
# obs_var_raw = self.env.observation_space.new_tensor_variable(
# 'obs',
# extra_dims=1,
# )
obs_var_raw = ext.new_tensor(
'obs', ndim=3, dtype=theano.config.floatX) # todo: check the dtype
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1,
)
# this will have to be the advantage every self.period timesteps
advantage_var = ext.new_tensor('advantage',
ndim=1,
dtype=theano.config.floatX)
obs_var_sparse = ext.new_tensor(
'sparse_obs',
ndim=2,
dtype=theano.config.
floatX # todo: check this with carlos, refer to discrete.py in rllab.spaces
)
assert isinstance(self.policy, HierarchicalPolicy)
# todo: assumptions: 1 trajectory, which is a multiple of p; that the obs_var_probs is valid
# undoing the reshape, so that batch sampling is ok
obs_var = TT.reshape(obs_var_raw, [
obs_var_raw.shape[0] * obs_var_raw.shape[1], obs_var_raw.shape[2]
])
# obs_var = obs_var_raw
# i, j should contain the probability of latent j at time step self.period*i
# should be a len(obs)//self.period by len(self.latent) tensor
latent_probs = self.policy.manager.dist_info_sym(
obs_var_sparse)['prob']
# get the distribution parameters
# dist_info_vars = []
# for latent in self.latents:
# self.policy.low_policy.set_latent_train(latent)
# dist_info_vars.append(self.policy.low_policy.dist_info_sym(obs_var))
# hopefully the above line takes multiple samples, and state_info_vars not needed as input
dist_info_vars = self.policy.low_policy.dist_info_sym_all_latents(
obs_var)
probs = [
TT.exp(self.diagonal.log_likelihood_sym(action_var, dist_info))
for dist_info in dist_info_vars
]
# need to reshape at the end
reshaped_probs = [
TT.reshape(prob, [obs_var.shape[0] // self.period, self.period])
for prob in probs
]
# now, multiply out each row and concatenate
subtrajectory_probs = TT.stack([
TT.prod(reshaped_prob, axis=1) for reshaped_prob in reshaped_probs
],
axis=1)
# shape error might come out of here
# elementwise multiplication, then sum up each individual row and take log
likelihood = TT.log(TT.sum(subtrajectory_probs * latent_probs, axis=1))
surr_loss = -TT.mean(likelihood * advantage_var)
input_list = [obs_var_raw, obs_var_sparse, action_var, advantage_var]
# npo has state_info_vars and old_dist_info_vars, I don't think I need them until I go for NPO/TRPO
self.optimizer.update_opt(loss=surr_loss,
target=self.policy,
inputs=input_list)
return dict()
#do the optimization
def optimize_policy(self, itr, samples_data):
assert len(samples_data) // self.period == 0
# note that I have to do extra preprocessing to the advantages, and also create obs_var_sparse
input_values = tuple(
ext.extract(samples_data, "observations", "actions", "advantages"))
# print(input_values[0].shape)
obs_raw = input_values[0].reshape(
input_values[0].shape[0] // self.period, self.period,
input_values[0].shape[1])
# obs_raw = input_values[0]
obs_sparse = input_values[0].take(
[i for i in range(0, input_values[0].shape[0], self.period)],
axis=0)
advantage_sparse = np.sum(input_values[2].reshape(
[input_values[2].shape[0] // self.period, self.period]),
axis=1)
all_input_values = (obs_raw, obs_sparse, input_values[1],
advantage_sparse)
loss_before = self.optimizer.loss(all_input_values)
self.optimizer.optimize(all_input_values)
loss_after = self.optimizer.loss(all_input_values)
logger.record_tabular('LossBefore', loss_before)
logger.record_tabular('LossAfter', loss_after)
logger.record_tabular('dLoss', loss_before - loss_after)
return dict()
def optimize_manager(self, itr, samples_data):
pass
def optimize_snn(self, itr, samples_data):
pass
def get_itr_snapshot(self, itr, samples_data):
return dict(itr=itr,
policy=self.policy,
baseline=self.baseline,
env=self.env)
def log_diagnostics(self, paths):
#paths obtained by self.sampler.obtain_samples
BatchPolopt.log_diagnostics(self, paths)
class PG_concurrent_approx(BatchPolopt, Serializable
): # todo: should this implement serializable?
"""
Designed to enable concurrent training of a SNN that parameterizes skills
and also train the manager at the same time
Note that, if I'm not trying to do the sample approximation of the weird log of sum term,
I don't need to know which skill was picked, just need to know the action
"""
# double check this constructor later
def __init__(
self,
optimizer=None,
optimizer_args=None,
step_size=1e-2,
num_latents=6,
latents=None, # some sort of iterable of the actual latent vectors
period=10, # how often I choose a latent
truncate_local_is_ratio=None,
use_skill_dependent_baseline=False,
**kwargs):
Serializable.quick_init(self, locals())
if optimizer is None:
default_args = dict(batch_size=None, max_epochs=1)
if optimizer_args is None:
optimizer_args = default_args
else:
optimizer_args = dict(default_args, **optimizer_args)
optimizer = FirstOrderOptimizer(learning_rate=step_size,
**optimizer_args)
self.optimizer = optimizer
self.step_size = step_size
self.truncate_local_is_ratio = truncate_local_is_ratio
super(PG_concurrent_approx,
self).__init__(**kwargs) # not sure if this line is correct
self.num_latents = kwargs['policy'].latent_dim
self.latents = latents
self.period = period
# todo: fix this sampler stuff
self.sampler = HierBatchSampler(self, self.period)
# i hope this is right
self.diagonal = DiagonalGaussian(
self.policy.low_policy.action_space.flat_dim)
self.debug_fns = []
assert isinstance(self.policy, HierarchicalPolicy)
if self.policy is not None:
self.period = self.policy.period
assert self.policy.period == self.period
self.trainable_manager = self.policy.trainable_manager
# skill dependent baseline
self.use_skill_dependent_baseline = use_skill_dependent_baseline
if use_skill_dependent_baseline:
curr_env = kwargs['env']
skill_dependent_action_space = curr_env.action_space
skill_dependent_obs_space_dim = (
(curr_env.observation_space.shape[0] + 1) * self.num_latents, )
skill_dependent_obs_space = Box(
-1.0, 1.0, shape=skill_dependent_obs_space_dim)
skill_depdendent_env_spec = EnvSpec(skill_dependent_obs_space,
skill_dependent_action_space)
self.skill_dependent_baseline = LinearFeatureBaseline(
env_spec=skill_depdendent_env_spec)
# initialize the computation graph
# optimize is run on >= 1 trajectory at a time
def init_opt(self):
# obs_var_raw = self.env.observation_space.new_tensor_variable(
# 'obs',
# extra_dims=1,
# )
obs_var_raw = ext.new_tensor(
'obs', ndim=3, dtype=theano.config.floatX) # todo: check the dtype
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1,
)
# this will have to be the advantage every self.period timesteps
advantage_var_sparse = ext.new_tensor('sparse_advantage',
ndim=1,
dtype=theano.config.floatX)
advantage_var = ext.new_tensor('advantage',
ndim=1,
dtype=theano.config.floatX)
obs_var_sparse = ext.new_tensor(
'sparse_obs',
ndim=2,
dtype=theano.config.
floatX # todo: check this with carlos, refer to discrete.py in rllab.spaces
)
latent_var_sparse = ext.new_tensor('sparse_latent',
ndim=2,
dtype=theano.config.floatX)
latent_var = ext.new_tensor('latents',
ndim=2,
dtype=theano.config.floatX)
assert isinstance(self.policy, HierarchicalPolicy)
# todo: assumptions: 1 trajectory, which is a multiple of p; that the obs_var_probs is valid
# undoing the reshape, so that batch sampling is ok
obs_var = TT.reshape(obs_var_raw, [
obs_var_raw.shape[0] * obs_var_raw.shape[1], obs_var_raw.shape[2]
])
# obs_var = obs_var_raw
#############################################################
### calculating the manager portion of the surrogate loss ###
#############################################################
# i, j should contain the probability of latent j at time step self.period*i
# should be a len(obs)//self.period by len(self.latent) tensor
latent_probs = self.policy.manager.dist_info_sym(
obs_var_sparse)['prob']
actual_latent_probs = TT.sum(latent_probs * latent_var_sparse, axis=1)
if self.trainable_manager:
manager_surr_loss = -TT.mean(
TT.log(actual_latent_probs) * advantage_var_sparse)
else:
manager_surr_loss = 0
############################################################
### calculating the skills portion of the surrogate loss ###
############################################################
# get the distribution parameters
# dist_info_vars = []
# for latent in self.latents:
# self.policy.low_policy.set_latent_train(latent)
# dist_info_vars.append(self.policy.low_policy.dist_info_sym(obs_var))
# hopefully the above line takes multiple samples, and state_info_vars not needed as input
dist_info_vars = self.policy.low_policy.dist_info_sym_all_latents(
obs_var)
probs = TT.stack([
self.diagonal.log_likelihood_sym(action_var, dist_info)
for dist_info in dist_info_vars
],
axis=1)
# todo: verify that dist_info_vars is in order
actual_action_log_probs = TT.sum(probs * latent_var, axis=1)
skill_surr_loss = -TT.mean(actual_action_log_probs * advantage_var)
surr_loss = manager_surr_loss / self.period + skill_surr_loss # so that the relative magnitudes are correct
input_list = [
obs_var_raw, obs_var_sparse, action_var, advantage_var,
advantage_var_sparse, latent_var, latent_var_sparse
]
# input_list = [obs_var_raw, obs_var_sparse, action_var, advantage_var]
# npo has state_info_vars and old_dist_info_vars, I don't think I need them until I go for NPO/TRPO
self.optimizer.update_opt(loss=surr_loss,
target=self.policy,
inputs=input_list)
return dict()
# do the optimization
def optimize_policy(self, itr, samples_data):
# import IPython; IPython.embed()
print(len(samples_data['observations']), self.period)
assert len(samples_data['observations']) % self.period == 0
# note that I have to do extra preprocessing to the advantages, and also create obs_var_sparse
if self.use_skill_dependent_baseline:
input_values = tuple(
ext.extract(samples_data, "observations", "actions",
"advantages", "agent_infos", "skill_advantages"))
else:
input_values = tuple(
ext.extract(samples_data, "observations", "actions",
"advantages", "agent_infos"))
# print(input_values[0].shape)
obs_raw = input_values[0].reshape(
input_values[0].shape[0] // self.period, self.period,
input_values[0].shape[1])
# obs_raw = input_values[0]
obs_sparse = input_values[0].take(
[i for i in range(0, input_values[0].shape[0], self.period)],
axis=0)
advantage_sparse = input_values[2].reshape(
[input_values[2].shape[0] // self.period, self.period])[:, 0]
latents = input_values[3]['latents']
latents_sparse = latents.take(
[i for i in range(0, latents.shape[0], self.period)], axis=0)
if self.use_skill_dependent_baseline:
all_input_values = (obs_raw, obs_sparse, input_values[1],
input_values[4], advantage_sparse, latents,
latents_sparse)
else:
all_input_values = (obs_raw, obs_sparse, input_values[1],
input_values[2], advantage_sparse, latents,
latents_sparse)
loss_before = self.optimizer.loss(all_input_values)
self.optimizer.optimize(all_input_values)
loss_after = self.optimizer.loss(all_input_values)
logger.record_tabular('LossBefore', loss_before)
logger.record_tabular('LossAfter', loss_after)
logger.record_tabular('dLoss', loss_before - loss_after)
return dict()
def get_itr_snapshot(self, itr, samples_data):
return dict(itr=itr,
policy=self.policy,
baseline=self.baseline,
env=self.env)
def log_diagnostics(self, paths):
# paths obtained by self.sampler.obtain_samples
BatchPolopt.log_diagnostics(self, paths)
class Hippo(BatchPolopt):
def __init__(
self,
optimizer=None,
optimizer_args=None,
step_size=0.0003,
latents=None, # some sort of iterable of the actual latent vectors
average_period=10, # average over all the periods
truncate_local_is_ratio=None,
epsilon=0.1,
train_pi_iters=80,
use_skill_dependent_baseline=False,
**kwargs):
if optimizer is None:
if optimizer_args is None:
# optimizer_args = dict()
optimizer_args = dict(batch_size=None)
self.optimizer = FirstOrderOptimizer(learning_rate=step_size,
max_epochs=train_pi_iters,
**optimizer_args)
self.step_size = step_size
self.truncate_local_is_ratio = truncate_local_is_ratio
self.epsilon = epsilon
super(Hippo,
self).__init__(**kwargs) # not sure if this line is correct
self.num_latents = kwargs['policy'].latent_dim
self.latents = latents
self.average_period = average_period
# import pdb; pdb.set_trace()
self.sampler = BatchSampler(self)
# i hope this is right
self.diagonal = DiagonalGaussian(
self.policy.low_policy.action_space.flat_dim)
self.debug_fns = []
self.use_skill_dependent_baseline = use_skill_dependent_baseline
assert isinstance(self.policy, HierarchicalPolicy)
self.old_policy = copy.deepcopy(self.policy)
def init_opt(self):
obs_var = ext.new_tensor(
'obs', ndim=2, dtype=theano.config.floatX) # todo: check the dtype
manager_obs_var = ext.new_tensor('manager_obs',
ndim=2,
dtype=theano.config.floatX)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1,
)
# this will have to be the advantage every time the manager makes a decision
manager_advantage_var = ext.new_tensor('manager_advantage',
ndim=1,
dtype=theano.config.floatX)
skill_advantage_var = ext.new_tensor('skill_advantage',
ndim=1,
dtype=theano.config.floatX)
latent_var_sparse = ext.new_tensor('sparse_latent',
ndim=2,
dtype=theano.config.floatX)
latent_var = ext.new_tensor('latents',
ndim=2,
dtype=theano.config.floatX)
assert isinstance(self.policy, HierarchicalPolicy)
#############################################################
### calculating the manager portion of the surrogate loss ###
#############################################################
# i, j should contain the probability of latent j at time step self.period*i
# should be a len(obs)//self.period by len(self.latent) tensor
latent_probs = self.policy.manager.dist_info_sym(
manager_obs_var)['prob']
old_latent_probs = self.old_policy.manager.dist_info_sym(
manager_obs_var)['prob']
actual_latent_probs = TT.sum(latent_probs * latent_var_sparse, axis=1)
old_actual_latent_probs = TT.sum(old_latent_probs * latent_var_sparse,
axis=1)
lr = TT.exp(
TT.log(actual_latent_probs) - TT.log(old_actual_latent_probs))
manager_surr_loss_vector = TT.minimum(
lr * manager_advantage_var,
TT.clip(lr, 1 - self.epsilon, 1 + self.epsilon) *
manager_advantage_var)
manager_surr_loss = -TT.mean(manager_surr_loss_vector)
############################################################
### calculating the skills portion of the surrogate loss ###
############################################################
dist_info_vars = self.policy.low_policy.dist_info_sym_all_latents(
obs_var)
probs = TT.stack([
self.diagonal.log_likelihood_sym(action_var, dist_info)
for dist_info in dist_info_vars
],
axis=1)
actual_action_log_probs = TT.sum(
probs * latent_var,
axis=1) # todo: verify that dist_info_vars is in order
# old policy stuff
old_dist_info_vars = self.old_policy.low_policy.dist_info_sym_all_latents(
obs_var)
old_probs = TT.stack([
self.diagonal.log_likelihood_sym(action_var, dist_info)
for dist_info in old_dist_info_vars
],
axis=1)
old_actual_action_log_probs = TT.sum(old_probs * latent_var, axis=1)
skill_lr = TT.exp(actual_action_log_probs -
old_actual_action_log_probs)
skill_surr_loss_vector = TT.minimum(
skill_lr * skill_advantage_var,
TT.clip(skill_lr, 1 - self.epsilon, 1 + self.epsilon) *
skill_advantage_var)
skill_surr_loss = -TT.mean(skill_surr_loss_vector)
surr_loss = manager_surr_loss / self.average_period + skill_surr_loss
input_list = [
obs_var, manager_obs_var, action_var, manager_advantage_var,
skill_advantage_var, latent_var, latent_var_sparse
]
self.optimizer.update_opt(loss=surr_loss,
target=self.policy,
inputs=input_list)
return dict()
# do the optimization
def optimize_policy(self, itr, samples_data):
# print(len(samples_data['observations']), self.period)
# assert len(samples_data['observations']) % self.period == 0
assert not self.use_skill_dependent_baseline
# note that I have to do extra preprocessing to the advantages, and also create obs_var_sparse
input_values = tuple(
ext.extract(samples_data, "observations", "actions", "advantages",
"agent_infos"))
time_remaining = input_values[3]['time_remaining']
resampled_period = input_values[3]['resampled_period']
obs_var = np.insert(input_values[0],
self.policy.obs_robot_dim,
time_remaining,
axis=1)
manager_obs_var = obs_var[resampled_period]
action_var = input_values[1]
manager_adv_var = input_values[2][resampled_period]
skill_adv_var = input_values[2]
latent_var = input_values[3]['latents']
latent_var_sparse = latent_var[resampled_period]
all_input_values = (obs_var, manager_obs_var, action_var,
manager_adv_var, skill_adv_var, latent_var,
latent_var_sparse)
# todo: assign current parameters to old policy; does this work?
old_param_values = self.policy.get_param_values()
self.old_policy.set_param_values(old_param_values)
loss_before = self.optimizer.loss(all_input_values)
self.optimizer.optimize(all_input_values)
loss_after = self.optimizer.loss(all_input_values)
logger.record_tabular('LossBefore', loss_before)
logger.record_tabular('LossAfter', loss_after)
logger.record_tabular('dLoss', loss_before - loss_after)
return dict()
def get_itr_snapshot(self, itr, samples_data):
return dict(itr=itr,
policy=self.policy,
baseline=self.baseline,
env=self.env)
def log_diagnostics(self, paths):
# paths obtained by self.sampler.obtain_samples
BatchPolopt.log_diagnostics(self, paths)
class StochasticGaussianMLPPolicy(StochasticPolicy, LasagnePowered,
Serializable):
def __init__(
self,
env_spec,
input_latent_vars=None,
hidden_sizes=(32, 32),
hidden_latent_vars=None,
learn_std=True,
init_std=1.0,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
):
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
# create network
mean_network = StochasticMLP(
input_shape=(obs_dim, ),
input_latent_vars=input_latent_vars,
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_latent_vars=hidden_latent_vars,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
)
l_mean = mean_network.output_layer
obs_var = mean_network.input_layer.input_var
l_log_std = ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
self._mean_network = mean_network
self._n_latent_layers = len(mean_network.latent_layers)
self._l_mean = l_mean
self._l_log_std = l_log_std
LasagnePowered.__init__(self, [l_mean, l_log_std])
super(StochasticGaussianMLPPolicy, self).__init__(env_spec)
outputs = self.dist_info_sym(mean_network.input_var)
latent_keys = sorted(
set(outputs.keys()).difference({"mean", "log_std"}))
extras = get_full_output([self._l_mean, self._l_log_std] +
self._mean_network.latent_layers, )[1]
latent_distributions = [
extras[layer]["distribution"]
for layer in self._mean_network.latent_layers
]
self._latent_keys = latent_keys
self._latent_distributions = latent_distributions
self._dist = DiagonalGaussian(action_dim)
self._f_dist_info = ext.compile_function(
inputs=[obs_var],
outputs=outputs,
)
self._f_dist_info_givens = None
@property
def latent_layers(self):
return self._mean_network.latent_layers
@property
def latent_dims(self):
return self._mean_network.latent_dims
def dist_info(self, obs, state_infos=None):
if state_infos is None or len(state_infos) == 0:
return self._f_dist_info(obs)
if self._f_dist_info_givens is None:
# compile function
obs_var = self._mean_network.input_var
latent_keys = [
"latent_%d" % idx for idx in range(self._n_latent_layers)
]
latent_vars = [
TT.matrix("latent_%d" % idx)
for idx in range(self._n_latent_layers)
]
latent_dict = dict(list(zip(latent_keys, latent_vars)))
self._f_dist_info_givens = ext.compile_function(
inputs=[obs_var] + latent_vars,
outputs=self.dist_info_sym(obs_var, latent_dict),
)
latent_vals = []
for idx in range(self._n_latent_layers):
latent_vals.append(state_infos["latent_%d" % idx])
return self._f_dist_info_givens(*[obs] + latent_vals)
def reset(self): #here I would sample a latents var.
# sample latents
# store it in self.something that then goes to all the others
pass
def dist_info_sym(self, obs_var, state_info_vars=None):
if state_info_vars is not None:
latent_givens = {
latent_layer: state_info_vars["latent_%d" % idx]
for idx, latent_layer in enumerate(
self._mean_network.latent_layers)
}
latent_dist_infos = dict()
for idx, latent_layer in enumerate(
self._mean_network.latent_layers):
cur_dist_info = dict()
prefix = "latent_%d_" % idx
for k, v in state_info_vars.items():
if k.startswith(prefix):
cur_dist_info[k[len(prefix):]] = v
latent_dist_infos[latent_layer] = cur_dist_info
else:
latent_givens = dict()
latent_dist_infos = dict()
all_outputs, extras = get_full_output(
[self._l_mean, self._l_log_std] + self._mean_network.latent_layers,
inputs={self._mean_network._l_in: obs_var},
latent_givens=latent_givens,
latent_dist_infos=latent_dist_infos,
)
mean_var = all_outputs[0]
log_std_var = all_outputs[1]
latent_vars = all_outputs[2:]
latent_dist_infos = []
for latent_layer in self._mean_network.latent_layers:
latent_dist_infos.append(extras[latent_layer]["dist_info"])
output_dict = dict(mean=mean_var, log_std=log_std_var)
for idx, latent_var, latent_dist_info in zip(itertools.count(),
latent_vars,
latent_dist_infos):
output_dict["latent_%d" % idx] = latent_var
for k, v in latent_dist_info.items():
output_dict["latent_%d_%s" % (idx, k)] = v
return output_dict
def kl_sym(self, old_dist_info_vars, new_dist_info_vars):
"""
Compute the symbolic KL divergence of distributions of both the actions and the latents variables
"""
kl = self._dist.kl_sym(old_dist_info_vars, new_dist_info_vars)
for idx, latent_dist in enumerate(self._latent_distributions):
# collect dist info for each latents variable
prefix = "latent_%d_" % idx
old_latent_dist_info = {
k[len(prefix):]: v
for k, v in old_dist_info_vars.items() if k.startswith(prefix)
}
new_latent_dist_info = {
k[len(prefix):]: v
for k, v in new_dist_info_vars.items() if k.startswith(prefix)
}
kl += latent_dist.kl_sym(old_latent_dist_info,
new_latent_dist_info)
return kl
def likelihood_ratio_sym(self, action_var, old_dist_info_vars,
new_dist_info_vars):
"""
Compute the symbolic likelihood ratio of both the actions and the latents variables.
"""
lr = self._dist.likelihood_ratio_sym(action_var, old_dist_info_vars,
new_dist_info_vars)
for idx, latent_dist in enumerate(self._latent_distributions):
latent_var = old_dist_info_vars["latent_%d" % idx]
prefix = "latent_%d_" % idx
old_latent_dist_info = {
k[len(prefix):]: v
for k, v in old_dist_info_vars.items() if k.startswith(prefix)
}
new_latent_dist_info = {
k[len(prefix):]: v
for k, v in new_dist_info_vars.items() if k.startswith(prefix)
}
lr *= latent_dist.likelihood_ratio_sym(latent_var,
old_latent_dist_info,
new_latent_dist_info)
return lr
def log_likelihood(self, actions, dist_info, action_only=False):
"""
Computes the log likelihood of both the actions and the latents variables, unless action_only is set to True,
in which case it will only compute the log likelihood of the actions.
:return:
"""
logli = self._dist.log_likelihood(actions, dist_info)
if not action_only:
for idx, latent_dist in enumerate(self._latent_distributions):
latent_var = dist_info["latent_%d" % idx]
prefix = "latent_%d_" % idx
latent_dist_info = {
k[len(prefix):]: v
for k, v in dist_info.items() if k.startswith(prefix)
}
logli += latent_dist.log_likelihood(latent_var,
latent_dist_info)
return logli
def log_likelihood_sym(self, action_var, dist_info_vars):
logli = self._dist.log_likelihood_sym(action_var, dist_info_vars)
for idx, latent_dist in enumerate(self._latent_distributions):
latent_var = dist_info_vars["latent_%d" % idx]
prefix = "latent_%d_" % idx
latent_dist_info = {
k[len(prefix):]: v
for k, v in dist_info_vars.items() if k.startswith(prefix)
}
logli += latent_dist.log_likelihood_sym(latent_var,
latent_dist_info)
return logli
def entropy(self, dist_info):
ent = self._dist.entropy(dist_info)
for idx, latent_dist in enumerate(self._latent_distributions):
prefix = "latent_%d_" % idx
latent_dist_info = {
k[len(prefix):]: v
for k, v in dist_info.items() if k.startswith(prefix)
}
ent += latent_dist.entropy(latent_dist_info)
return ent
@property
def dist_info_keys(self):
return ["mean", "log_std"] + self._latent_keys
@overrides
def get_action(self, observation):
actions, outputs = self.get_actions([observation])
return actions[0], {k: v[0] for k, v in outputs.items()}
def get_actions(self, observations):
outputs = self._f_dist_info(observations)
mean = outputs["mean"]
log_std = outputs["log_std"]
rnd = np.random.normal(size=mean.shape)
actions = rnd * np.exp(log_std) + mean
return actions, outputs
def log_diagnostics(self, paths):
log_stds = np.vstack(
[path["agent_infos"]["log_std"] for path in paths])
logger.record_tabular('AveragePolicyStd', np.mean(np.exp(log_stds)))
@property
def distribution(self):
"""
We set the distribution to the policy itself since we need some behavior different from a usual diagonal
Gaussian distribution.
"""
return self
@property
def state_info_keys(self):
return self._latent_keys
