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projections_orig is stolen from Fabian fro reference; projections is

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begginning of own implementation
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Dominik Moritz Roth 2022-06-16 10:59:26 +02:00
parent 9a99962176
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projections_orig/__init__.py Normal file
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# Copyright (c) 2021 Robert Bosch GmbH
# Author: Fabian Otto
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.

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projections_orig/base_projection_layer.py Normal file
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# Copyright (c) 2021 Robert Bosch GmbH
# Author: Fabian Otto
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import copy
import math
import torch as ch
from typing import Tuple, Union
from trust_region_projections.models.policy.abstract_gaussian_policy import AbstractGaussianPolicy
from trust_region_projections.utils.network_utils import get_optimizer
from trust_region_projections.utils.projection_utils import gaussian_kl, get_entropy_schedule
from trust_region_projections.utils.torch_utils import generate_minibatches, select_batch, tensorize
def entropy_inequality_projection(policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
beta: Union[float, ch.Tensor]):
"""
Projects std to satisfy an entropy INEQUALITY constraint.
Args:
policy: policy instance
p: current distribution
beta: target entropy for EACH std or general bound for all stds
Returns:
projected std that satisfies the entropy bound
"""
mean, std = p
k = std.shape[-1]
batch_shape = std.shape[:-2]
ent = policy.entropy(p)
mask = ent < beta
# if nothing has to be projected skip computation
if (~mask).all():
return p
alpha = ch.ones(batch_shape, dtype=std.dtype, device=std.device)
alpha[mask] = ch.exp((beta[mask] - ent[mask]) / k)
proj_std = ch.einsum('ijk,i->ijk', std, alpha)
return mean, ch.where(mask[..., None, None], proj_std, std)
def entropy_equality_projection(policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
beta: Union[float, ch.Tensor]):
"""
Projects std to satisfy an entropy EQUALITY constraint.
Args:
policy: policy instance
p: current distribution
beta: target entropy for EACH std or general bound for all stds
Returns:
projected std that satisfies the entropy bound
"""
mean, std = p
k = std.shape[-1]
ent = policy.entropy(p)
alpha = ch.exp((beta - ent) / k)
proj_std = ch.einsum('ijk,i->ijk', std, alpha)
return mean, proj_std
def mean_projection(mean: ch.Tensor, old_mean: ch.Tensor, maha: ch.Tensor, eps: ch.Tensor):
"""
Projects the mean based on the Mahalanobis objective and trust region.
Args:
mean: current mean vectors
old_mean: old mean vectors
maha: Mahalanobis distance between the two mean vectors
eps: trust region bound
Returns:
projected mean that satisfies the trust region
"""
batch_shape = mean.shape[:-1]
mask = maha > eps
################################################################################################################
# mean projection maha
# if nothing has to be projected skip computation
if mask.any():
omega = ch.ones(batch_shape, dtype=mean.dtype, device=mean.device)
omega[mask] = ch.sqrt(maha[mask] / eps) - 1.
omega = ch.max(-omega, omega)[..., None]
m = (mean + omega * old_mean) / (1 + omega + 1e-16)
proj_mean = ch.where(mask[..., None], m, mean)
else:
proj_mean = mean
return proj_mean
class BaseProjectionLayer(object):
def __init__(self,
proj_type: str = "",
mean_bound: float = 0.03,
cov_bound: float = 1e-3,
trust_region_coeff: float = 0.0,
scale_prec: bool = True,
entropy_schedule: Union[None, str] = None,
action_dim: Union[None, int] = None,
total_train_steps: Union[None, int] = None,
target_entropy: float = 0.0,
temperature: float = 0.5,
entropy_eq: bool = False,
entropy_first: bool = False,
do_regression: bool = False,
regression_iters: int = 1000,
regression_lr: int = 3e-4,
optimizer_type_reg: str = "adam",
cpu: bool = True,
dtype: ch.dtype = ch.float32,
):
"""
Base projection layer, which can be used to compute metrics for non-projection approaches.
Args:
proj_type: Which type of projection to use. None specifies no projection and uses the TRPO objective.
mean_bound: projection bound for the step size w.r.t. mean
cov_bound: projection bound for the step size w.r.t. covariance matrix
trust_region_coeff: Coefficient for projection regularization loss term.
scale_prec: If true used mahalanobis distance for projections instead of euclidean with Sigma_old^-1.
entropy_schedule: Schedule type for entropy projection, one of 'linear', 'exp', None.
action_dim: number of action dimensions to scale exp decay correctly.
total_train_steps: total number of training steps to compute appropriate decay over time.
target_entropy: projection bound for the entropy of the covariance matrix
temperature: temperature decay for exponential entropy bound
entropy_eq: Use entropy equality constraints.
entropy_first: Project entropy before trust region.
do_regression: Conduct additional regression steps after the the policy steps to match projection and policy.
regression_iters: Number of regression steps.
regression_lr: Regression learning rate.
optimizer_type_reg: Optimizer for regression.
cpu: Compute on CPU only.
dtype: Data type to use, either of float32 or float64. The later might be necessary for higher
dimensions in order to learn the full covariance.
"""
# projection and bounds
self.proj_type = proj_type
self.mean_bound = tensorize(mean_bound, cpu=cpu, dtype=dtype)
self.cov_bound = tensorize(cov_bound, cpu=cpu, dtype=dtype)
self.trust_region_coeff = trust_region_coeff
self.scale_prec = scale_prec
# projection utils
assert (action_dim and total_train_steps) if entropy_schedule else True
self.entropy_proj = entropy_equality_projection if entropy_eq else entropy_inequality_projection
self.entropy_schedule = get_entropy_schedule(entropy_schedule, total_train_steps, dim=action_dim)
self.target_entropy = tensorize(target_entropy, cpu=cpu, dtype=dtype)
self.entropy_first = entropy_first
self.entropy_eq = entropy_eq
self.temperature = temperature
self._initial_entropy = None
# regression
self.do_regression = do_regression
self.regression_iters = regression_iters
self.lr_reg = regression_lr
self.optimizer_type_reg = optimizer_type_reg
def __call__(self, policy, p: Tuple[ch.Tensor, ch.Tensor], q, step, *args, **kwargs):
# entropy_bound = self.policy.entropy(q) - self.target_entropy
entropy_bound = self.entropy_schedule(self.initial_entropy, self.target_entropy, self.temperature,
step) * p[0].new_ones(p[0].shape[0])
return self._projection(policy, p, q, self.mean_bound, self.cov_bound, entropy_bound, **kwargs)
def _trust_region_projection(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
q: Tuple[ch.Tensor, ch.Tensor], eps: ch.Tensor, eps_cov: ch.Tensor, **kwargs):
"""
Hook for implementing the specific trust region projection
Args:
policy: policy instance
p: current distribution
q: old distribution
eps: mean trust region bound
eps_cov: covariance trust region bound
**kwargs:
Returns:
projected
"""
return p
# @final
def _projection(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
q: Tuple[ch.Tensor, ch.Tensor], eps: ch.Tensor, eps_cov: ch.Tensor, beta: ch.Tensor, **kwargs):
"""
Template method with hook _trust_region_projection() to encode specific functionality.
(Optional) entropy projection is executed before or after as specified by entropy_first.
Do not override this. For Python >= 3.8 you can use the @final decorator to enforce not overwriting.
Args:
policy: policy instance
p: current distribution
q: old distribution
eps: mean trust region bound
eps_cov: covariance trust region bound
beta: entropy bound
**kwargs:
Returns:
projected mean, projected std
"""
####################################################################################################################
# entropy projection in the beginning
if self.entropy_first:
p = self.entropy_proj(policy, p, beta)
####################################################################################################################
# trust region projection for mean and cov bounds
proj_mean, proj_std = self._trust_region_projection(policy, p, q, eps, eps_cov, **kwargs)
####################################################################################################################
# entropy projection in the end
if self.entropy_first:
return proj_mean, proj_std
return self.entropy_proj(policy, (proj_mean, proj_std), beta)
@property
def initial_entropy(self):
return self._initial_entropy
@initial_entropy.setter
def initial_entropy(self, entropy):
if self.initial_entropy is None:
self._initial_entropy = entropy
def trust_region_value(self, policy, p, q):
"""
Computes the KL divergence between two Gaussian distributions p and q.
Args:
policy: policy instance
p: current distribution
q: old distribution
Returns:
Mean and covariance part of the trust region metric.
"""
return gaussian_kl(policy, p, q)
def get_trust_region_loss(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
proj_p: Tuple[ch.Tensor, ch.Tensor]):
"""
Compute the trust region loss to ensure policy output and projection stay close.
Args:
policy: policy instance
proj_p: projected distribution
p: predicted distribution from network output
Returns:
trust region loss
"""
p_target = (proj_p[0].detach(), proj_p[1].detach())
mean_diff, cov_diff = self.trust_region_value(policy, p, p_target)
delta_loss = (mean_diff + cov_diff if policy.contextual_std else mean_diff).mean()
return delta_loss * self.trust_region_coeff
def compute_metrics(self, policy, p, q) -> dict:
"""
Returns dict with constraint metrics.
Args:
policy: policy instance
p: current distribution
q: old distribution
Returns:
dict with constraint metrics
"""
with ch.no_grad():
entropy_old = policy.entropy(q)
entropy = policy.entropy(p)
mean_kl, cov_kl = gaussian_kl(policy, p, q)
kl = mean_kl + cov_kl
mean_diff, cov_diff = self.trust_region_value(policy, p, q)
combined_constraint = mean_diff + cov_diff
entropy_diff = entropy_old - entropy
return {'kl': kl.detach().mean(),
'constraint': combined_constraint.mean(),
'mean_constraint': mean_diff.mean(),
'cov_constraint': cov_diff.mean(),
'entropy': entropy.mean(),
'entropy_diff': entropy_diff.mean(),
'kl_max': kl.max(),
'constraint_max': combined_constraint.max(),
'mean_constraint_max': mean_diff.max(),
'cov_constraint_max': cov_diff.max(),
'entropy_max': entropy.max(),
'entropy_diff_max': entropy_diff.max()
}
def trust_region_regression(self, policy: AbstractGaussianPolicy, obs: ch.Tensor, q: Tuple[ch.Tensor, ch.Tensor],
n_minibatches: int, global_steps: int):
"""
Take additional regression steps to match projection output and policy output.
The policy parameters are updated in-place.
Args:
policy: policy instance
obs: collected observations from trajectories
q: old distributions
n_minibatches: split the rollouts into n_minibatches.
global_steps: current number of steps, required for projection
Returns:
dict with mean of regession loss
"""
if not self.do_regression:
return {}
policy_unprojected = copy.deepcopy(policy)
optim_reg = get_optimizer(self.optimizer_type_reg, policy_unprojected.parameters(), learning_rate=self.lr_reg)
optim_reg.reset()
reg_losses = obs.new_tensor(0.)
# get current projected values --> targets for regression
p_flat = policy(obs)
p_target = self(policy, p_flat, q, global_steps)
for _ in range(self.regression_iters):
batch_indices = generate_minibatches(obs.shape[0], n_minibatches)
# Minibatches SGD
for indices in batch_indices:
batch = select_batch(indices, obs, p_target[0], p_target[1])
b_obs, b_target_mean, b_target_std = batch
proj_p = (b_target_mean.detach(), b_target_std.detach())
p = policy_unprojected(b_obs)
# invert scaling with coeff here as we do not have to balance with other losses
loss = self.get_trust_region_loss(policy, p, proj_p) / self.trust_region_coeff
optim_reg.zero_grad()
loss.backward()
optim_reg.step()
reg_losses += loss.detach()
policy.load_state_dict(policy_unprojected.state_dict())
if not policy.contextual_std:
# set policy with projection value.
# In non-contextual cases we have only one cov, so the projection is the same.
policy.set_std(p_target[1][0])
steps = self.regression_iters * (math.ceil(obs.shape[0] / n_minibatches))
return {"regression_loss": (reg_losses / steps).detach()}

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projections_orig/frob_projection_layer.py Normal file
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# Copyright (c) 2021 Robert Bosch GmbH
# Author: Fabian Otto
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import torch as ch
from typing import Tuple
from trust_region_projections.models.policy.abstract_gaussian_policy import AbstractGaussianPolicy
from trust_region_projections.projections.base_projection_layer import BaseProjectionLayer, mean_projection
from trust_region_projections.utils.projection_utils import gaussian_frobenius
class FrobeniusProjectionLayer(BaseProjectionLayer):
def _trust_region_projection(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
q: Tuple[ch.Tensor, ch.Tensor], eps: ch.Tensor, eps_cov: ch.Tensor, **kwargs):
"""
Runs Frobenius projection layer and constructs cholesky of covariance
Args:
policy: policy instance
p: current distribution
q: old distribution
eps: (modified) kl bound/ kl bound for mean part
eps_cov: (modified) kl bound for cov part
beta: (modified) entropy bound
**kwargs:
Returns: mean, cov cholesky
"""
mean, chol = p
old_mean, old_chol = q
batch_shape = mean.shape[:-1]
####################################################################################################################
# precompute mean and cov part of frob projection, which are used for the projection.
mean_part, cov_part, cov, cov_old = gaussian_frobenius(policy, p, q, self.scale_prec, True)
################################################################################################################
# mean projection maha/euclidean
proj_mean = mean_projection(mean, old_mean, mean_part, eps)
################################################################################################################
# cov projection frobenius
cov_mask = cov_part > eps_cov
if cov_mask.any():
# alpha = ch.where(fro_norm_sq > eps_cov, ch.sqrt(fro_norm_sq / eps_cov) - 1., ch.tensor(1.))
eta = ch.ones(batch_shape, dtype=chol.dtype, device=chol.device)
eta[cov_mask] = ch.sqrt(cov_part[cov_mask] / eps_cov) - 1.
eta = ch.max(-eta, eta)
new_cov = (cov + ch.einsum('i,ijk->ijk', eta, cov_old)) / (1. + eta + 1e-16)[..., None, None]
proj_chol = ch.where(cov_mask[..., None, None], ch.cholesky(new_cov), chol)
else:
proj_chol = chol
return proj_mean, proj_chol
def trust_region_value(self, policy, p, q):
"""
Computes the Frobenius metric between two Gaussian distributions p and q.
Args:
policy: policy instance
p: current distribution
q: old distribution
Returns:
mean and covariance part of Frobenius metric
"""
return gaussian_frobenius(policy, p, q, self.scale_prec)
def get_trust_region_loss(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
proj_p: Tuple[ch.Tensor, ch.Tensor]):
mean_diff, _ = self.trust_region_value(policy, p, proj_p)
if policy.contextual_std:
# Compute MSE here, because we found the Frobenius norm tends to generate values that explode for the cov
cov_diff = (p[1] - proj_p[1]).pow(2).sum([-1, -2])
delta_loss = (mean_diff + cov_diff).mean()
else:
delta_loss = mean_diff.mean()
return delta_loss * self.trust_region_coeff

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projections_orig/kl_projection_layer.py Normal file
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import cpp_projection
import numpy as np
import torch as ch
from typing import Any, Tuple
from trust_region_projections.models.policy.abstract_gaussian_policy import AbstractGaussianPolicy
from trust_region_projections.projections.base_projection_layer import BaseProjectionLayer, mean_projection
from trust_region_projections.utils.projection_utils import gaussian_kl
from trust_region_projections.utils.torch_utils import get_numpy
class KLProjectionLayer(BaseProjectionLayer):
def _trust_region_projection(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
q: Tuple[ch.Tensor, ch.Tensor], eps: ch.Tensor, eps_cov: ch.Tensor, **kwargs):
"""
Runs KL projection layer and constructs cholesky of covariance
Args:
policy: policy instance
p: current distribution
q: old distribution
eps: (modified) kl bound/ kl bound for mean part
eps_cov: (modified) kl bound for cov part
**kwargs:
Returns:
projected mean, projected cov cholesky
"""
mean, std = p
old_mean, old_std = q
if not policy.contextual_std:
# only project first one to reduce number of numerical optimizations
std = std[:1]
old_std = old_std[:1]
################################################################################################################
# project mean with closed form
mean_part, _ = gaussian_kl(policy, p, q)
proj_mean = mean_projection(mean, old_mean, mean_part, eps)
cov = policy.covariance(std)
old_cov = policy.covariance(old_std)
if policy.is_diag:
proj_cov = KLProjectionGradFunctionDiagCovOnly.apply(cov.diagonal(dim1=-2, dim2=-1),
old_cov.diagonal(dim1=-2, dim2=-1),
eps_cov)
proj_std = proj_cov.sqrt().diag_embed()
else:
raise NotImplementedError("The KL projection currently does not support full covariance matrices.")
if not policy.contextual_std:
# scale first std back to batchsize
proj_std = proj_std.expand(mean.shape[0], -1, -1)
return proj_mean, proj_std
class KLProjectionGradFunctionDiagCovOnly(ch.autograd.Function):
projection_op = None
@staticmethod
def get_projection_op(batch_shape, dim, max_eval=100):
if not KLProjectionGradFunctionDiagCovOnly.projection_op:
KLProjectionGradFunctionDiagCovOnly.projection_op = \
cpp_projection.BatchedDiagCovOnlyProjection(batch_shape, dim, max_eval=max_eval)
return KLProjectionGradFunctionDiagCovOnly.projection_op
@staticmethod
def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:
std, old_std, eps_cov = args
batch_shape = std.shape[0]
dim = std.shape[-1]
cov_np = get_numpy(std)
old_std = get_numpy(old_std)
eps = get_numpy(eps_cov) * np.ones(batch_shape)
# p_op = cpp_projection.BatchedDiagCovOnlyProjection(batch_shape, dim)
# ctx.proj = projection_op
p_op = KLProjectionGradFunctionDiagCovOnly.get_projection_op(batch_shape, dim)
ctx.proj = p_op
proj_std = p_op.forward(eps, old_std, cov_np)
return std.new(proj_std)
@staticmethod
def backward(ctx: Any, *grad_outputs: Any) -> Any:
projection_op = ctx.proj
d_std, = grad_outputs
d_std_np = get_numpy(d_std)
d_std_np = np.atleast_2d(d_std_np)
df_stds = projection_op.backward(d_std_np)
df_stds = np.atleast_2d(df_stds)
return d_std.new(df_stds), None, None

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# Copyright (c) 2021 Robert Bosch GmbH
# Author: Fabian Otto
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import logging
import copy
import numpy as np
import torch as ch
from typing import Tuple, Union
from trust_region_projections.utils.projection_utils import gaussian_kl
from trust_region_projections.models.policy.abstract_gaussian_policy import AbstractGaussianPolicy
from trust_region_projections.projections.base_projection_layer import BaseProjectionLayer
from trust_region_projections.utils.torch_utils import torch_batched_trace
logger = logging.getLogger("papi_projection")
class PAPIProjection(BaseProjectionLayer):
def __init__(self,
proj_type: str = "",
mean_bound: float = 0.015,
cov_bound: float = 0.0,
entropy_eq: bool = False,
entropy_first: bool = True,
cpu: bool = True,
dtype: ch.dtype = ch.float32,
**kwargs
):
"""
PAPI projection, which can be used after each training epoch to satisfy the trust regions.
Args:
proj_type: Which type of projection to use. None specifies no projection and uses the TRPO objective.
mean_bound: projection bound for the step size,
PAPI only has a joint KL constraint, mean and cov bound are summed for this bound.
cov_bound: projection bound for the step size,
PAPI only has a joint KL constraint, mean and cov bound are summed for this bound.
entropy_eq: Use entropy equality constraints.
entropy_first: Project entropy before trust region.
cpu: Compute on CPU only.
dtype: Data type to use, either of float32 or float64. The later might be necessary for higher
dimensions in order to learn the full covariance.
"""
assert entropy_first
super().__init__(proj_type, mean_bound, cov_bound, 0.0, False, None, None, None, 0.0, 0.0, entropy_eq,
entropy_first, cpu, dtype)
self.last_policies = []
def __call__(self, policy, p, q, step=0, *args, **kwargs):
if kwargs.get("obs"):
self._papi_steps(policy, q, **kwargs)
else:
return p
def _trust_region_projection(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
q: Tuple[ch.Tensor, ch.Tensor], eps: Union[ch.Tensor, float],
eps_cov: Union[ch.Tensor, float], **kwargs):
"""
runs papi projection layer and constructs sqrt of covariance
Args:
policy: policy instance
p: current distribution
q: old distribution
eps: (modified) kl bound/ kl bound for mean part
eps_cov: (modified) kl bound for cov part
**kwargs:
Returns:
mean, cov sqrt
"""
mean, chol = p
old_mean, old_chol = q
intermed_mean = kwargs.get('intermed_mean')
dtype = mean.dtype
device = mean.device
dim = mean.shape[-1]
################################################################################################################
# Precompute basic matrices
# Joint bound
eps += eps_cov
I = ch.eye(dim, dtype=dtype, device=device)
old_precision = ch.cholesky_solve(I, old_chol)[0]
logdet_old = policy.log_determinant(old_chol)
cov = policy.covariance(chol)
################################################################################################################
# compute expected KL
maha_part, cov_part = gaussian_kl(policy, p, q)
maha_part = maha_part.mean()
cov_part = cov_part.mean()
if intermed_mean is not None:
maha_intermediate = 0.5 * policy.maha(intermed_mean, old_mean, old_chol).mean()
mm = ch.min(maha_part, maha_intermediate)
################################################################################################################
# matrix rotation/rescaling projection
if maha_part + cov_part > eps + 1e-6:
old_cov = policy.covariance(old_chol)
maha_delta = eps if intermed_mean is None else (eps - mm)
eta_rot = maha_delta / ch.max(maha_part + cov_part, ch.tensor(1e-16, dtype=dtype, device=device))
new_cov = (1 - eta_rot) * old_cov + eta_rot * cov
proj_chol = ch.cholesky(new_cov)
# recompute covariance part of KL for new chol
trace_term = 0.5 * (torch_batched_trace(old_precision @ new_cov) - dim).mean() # rotation difference
entropy_diff = 0.5 * (logdet_old - policy.log_determinant(proj_chol)).mean()
cov_part = trace_term + entropy_diff
else:
proj_chol = chol
################################################################################################################
# mean interpolation projection
if maha_part + cov_part > eps + 1e-6:
if intermed_mean is not None:
a = 0.5 * policy.maha(mean, intermed_mean, old_chol).mean()
b = 0.5 * ((mean - intermed_mean) @ old_precision @ (intermed_mean - old_mean).T).mean()
c = maha_intermediate - ch.max(eps - cov_part, ch.tensor(0., dtype=dtype, device=device))
eta_mean = (-b + ch.sqrt(ch.max(b * b - a * c, ch.tensor(1e-16, dtype=dtype, device=device)))) / \
ch.max(a, ch.tensor(1e-16, dtype=dtype, device=device))
else:
eta_mean = ch.sqrt(
ch.max(eps - cov_part, ch.tensor(1e-16, dtype=dtype, device=device)) /
ch.max(maha_part, ch.tensor(1e-16, dtype=dtype, device=device)))
else:
eta_mean = ch.tensor(1., dtype=dtype, device=device)
return eta_mean, proj_chol
def _papi_steps(self, policy: AbstractGaussianPolicy, q: Tuple[ch.Tensor, ch.Tensor], obs: ch.Tensor, lr_schedule,
lr_schedule_vf=None):
"""
Take PAPI steps after PPO finished its steps. Policy parameters are updated in-place.
Args:
policy: policy instance
q: old distribution
obs: collected observations from trajectories
lr_schedule: lr schedule for policy
lr_schedule_vf: lr schedule for vf
Returns:
"""
assert not policy.contextual_std
# save latest policy in history
self.last_policies.append(copy.deepcopy(policy))
################################################################################################################
# policy backtracking: out of last n policies and current one find one that satisfies the kl constraint
intermed_policy = None
n_backtracks = 0
for i, pi in enumerate(reversed(self.last_policies)):
p_prime = pi(obs)
mean_part, cov_part = pi.kl_divergence(p_prime, q)
if (mean_part + cov_part).mean() <= self.mean_bound + self.cov_bound:
intermed_policy = pi
n_backtracks = i
break
################################################################################################################
# LR update
# reduce learning rate when appropriate policy not within the last 4 epochs
if n_backtracks >= 4 or intermed_policy is None:
# Linear learning rate annealing
lr_schedule.step()
if lr_schedule_vf:
lr_schedule_vf.step()
if intermed_policy is None:
# pop last policy and make it current one, as the updated one was poor
# do not keep last policy in history, otherwise we could stack the same policy multiple times.
if len(self.last_policies) >= 1:
policy.load_state_dict(self.last_policies.pop().state_dict())
logger.warning(f"No suitable policy found in backtracking of {len(self.last_policies)} policies.")
return
################################################################################################################
# PAPI iterations
# We assume only non contextual covariances here, therefore we only need to project for one
q = (q[0], q[1][:1]) # (means, covs[:1])
# This is A from Alg. 2 [Akrour et al., 2019]
intermed_weight = intermed_policy.get_last_layer().detach().clone()
# This is A @ phi(s)
intermed_mean = p_prime[0].detach().clone()
entropy = policy.entropy(q)
entropy_bound = obs.new_tensor([-np.inf]) if entropy / self.initial_entropy > 0.5 \
else entropy - (self.mean_bound + self.cov_bound)
for _ in range(20):
eta, proj_chol = self._projection(intermed_policy, (p_prime[0], p_prime[1][:1]), q,
self.mean_bound, self.cov_bound, entropy_bound,
intermed_mean=intermed_mean)
intermed_policy.papi_weight_update(eta, intermed_weight)
intermed_policy.set_std(proj_chol[0])
p_prime = intermed_policy(obs)
policy.load_state_dict(intermed_policy.state_dict())

54
projections_orig/projection_factory.py Normal file
View File

@ -0,0 +1,54 @@
# Copyright (c) 2021 Robert Bosch GmbH
# Author: Fabian Otto
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
from trust_region_projections.projections.base_projection_layer import BaseProjectionLayer
from trust_region_projections.projections.frob_projection_layer import FrobeniusProjectionLayer
from trust_region_projections.projections.kl_projection_layer import KLProjectionLayer
from trust_region_projections.projections.papi_projection import PAPIProjection
from trust_region_projections.projections.w2_projection_layer import WassersteinProjectionLayer
def get_projection_layer(proj_type: str = "", **kwargs) -> BaseProjectionLayer:
"""
Factory to generate the projection layers for all projections.
Args:
proj_type: One of None/' ', 'ppo', 'papi', 'w2', 'w2_non_com', 'frob', 'kl', or 'entropy'
**kwargs: arguments for projection layer
Returns:
"""
if not proj_type or proj_type.isspace() or proj_type.lower() in ["ppo", "sac", "td3", "mpo", "entropy"]:
return BaseProjectionLayer(proj_type, **kwargs)
elif proj_type.lower() == "w2":
return WassersteinProjectionLayer(proj_type, **kwargs)
elif proj_type.lower() == "frob":
return FrobeniusProjectionLayer(proj_type, **kwargs)
elif proj_type.lower() == "kl":
return KLProjectionLayer(proj_type, **kwargs)
elif proj_type.lower() == "papi":
# papi has a different approach compared to our projections.
# It has to be applied after the training with PPO.
return PAPIProjection(proj_type, **kwargs)
else:
raise ValueError(
f"Invalid projection type {proj_type}."
f" Choose one of None/' ', 'ppo', 'papi', 'w2', 'w2_non_com', 'frob', 'kl', or 'entropy'.")

84
projections_orig/w2_projection_layer.py Normal file
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@ -0,0 +1,84 @@
# Copyright (c) 2021 Robert Bosch GmbH
# Author: Fabian Otto
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import torch as ch
from typing import Tuple
from trust_region_projections.models.policy.abstract_gaussian_policy import AbstractGaussianPolicy
from trust_region_projections.projections.base_projection_layer import BaseProjectionLayer, mean_projection
from trust_region_projections.utils.projection_utils import gaussian_wasserstein_commutative
class WassersteinProjectionLayer(BaseProjectionLayer):
def _trust_region_projection(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
q: Tuple[ch.Tensor, ch.Tensor], eps: ch.Tensor, eps_cov: ch.Tensor, **kwargs):
"""
Runs commutative Wasserstein projection layer and constructs sqrt of covariance
Args:
policy: policy instance
p: current distribution
q: old distribution
eps: (modified) kl bound/ kl bound for mean part
eps_cov: (modified) kl bound for cov part
**kwargs:
Returns:
mean, cov sqrt
"""
mean, sqrt = p
old_mean, old_sqrt = q
batch_shape = mean.shape[:-1]
####################################################################################################################
# precompute mean and cov part of W2, which are used for the projection.
# Both parts differ based on precision scaling.
# If activated, the mean part is the maha distance and the cov has a more complex term in the inner parenthesis.
mean_part, cov_part = gaussian_wasserstein_commutative(policy, p, q, self.scale_prec)
####################################################################################################################
# project mean (w/ or w/o precision scaling)
proj_mean = mean_projection(mean, old_mean, mean_part, eps)
####################################################################################################################
# project covariance (w/ or w/o precision scaling)
cov_mask = cov_part > eps_cov
if cov_mask.any():
# gradient issue with ch.where, it executes both paths and gives NaN gradient.
eta = ch.ones(batch_shape, dtype=sqrt.dtype, device=sqrt.device)
eta[cov_mask] = ch.sqrt(cov_part[cov_mask] / eps_cov) - 1.
eta = ch.max(-eta, eta)
new_sqrt = (sqrt + ch.einsum('i,ijk->ijk', eta, old_sqrt)) / (1. + eta + 1e-16)[..., None, None]
proj_sqrt = ch.where(cov_mask[..., None, None], new_sqrt, sqrt)
else:
proj_sqrt = sqrt
return proj_mean, proj_sqrt
def trust_region_value(self, policy, p, q):
"""
Computes the Wasserstein distance between two Gaussian distributions p and q.
Args:
policy: policy instance
p: current distribution
q: old distribution
Returns:
mean and covariance part of Wasserstein distance
"""
return gaussian_wasserstein_commutative(policy, p, q, scale_prec=self.scale_prec)

27
trl_pg/trl_pg.py
View File

@ -95,8 +95,7 @@ class TRL_PG(OnPolicyAlgorithm):
device: Union[th.device, str] = "auto",
# Different from PPO:
importance_ratio_clip: Union[float, None] = 0.2
#TODO: projection: BaseProjectionLayer = None,
projection: BaseProjectionLayer = None,
_init_setup_model: bool = True,
):
@ -161,7 +160,7 @@ class TRL_PG(OnPolicyAlgorithm):
self.target_kl = target_kl
# Different from PPO:
self.importance_ratio_clip = importance_ratio_clip or 0.0
self.projection = projection
if _init_setup_model:
self._setup_model()
@ -191,7 +190,9 @@ class TRL_PG(OnPolicyAlgorithm):
if self.clip_range_vf is not None:
clip_range_vf = self.clip_range_vf(self._current_progress_remaining)
surrogate_losses = []
entropy_losses = []
trust_region_losses = []
pg_losses, value_losses = [], []
clip_fractions = []
@ -221,10 +222,13 @@ class TRL_PG(OnPolicyAlgorithm):
# ratio between old and new policy, should be one at the first iteration
ratio = th.exp(log_prob - rollout_data.old_log_prob)
# Difference from PPO: We renamed 'policy_loss' to 'surrogate_loss'
# clipped surrogate loss
policy_loss_1 = advantages * ratio
policy_loss_2 = advantages * th.clamp(ratio, 1 - clip_range, 1 + clip_range)
policy_loss = -th.min(policy_loss_1, policy_loss_2).mean()
surrogate_loss_1 = advantages * ratio
surrogate_loss_2 = advantages * th.clamp(ratio, 1 - clip_range, 1 + clip_range)
surrogate_loss = -th.min(policy_loss_1, policy_loss_2).mean()
surrogate_losses.append(surrogate_loss.item())
# Logging
pg_losses.append(policy_loss.item())
@ -253,7 +257,14 @@ class TRL_PG(OnPolicyAlgorithm):
entropy_losses.append(entropy_loss.item())
loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss
# Difference to PPO: Added trust_region_loss; policy_loss includes entropy_loss + trust_region_loss
trust_region_loss = self.projection.get_trust_region_loss()#TODO: params
trust_region_losses.append(trust_region_loss.item())
policy_loss = surrogate_loss + self.ent_coef * entropy_loss + trust_region_loss
loss = policy_loss + self.vf_coef * value_loss
# Calculate approximate form of reverse KL Divergence for early stopping
# see issue #417: https://github.com/DLR-RM/stable-baselines3/issues/417
@ -284,7 +295,9 @@ class TRL_PG(OnPolicyAlgorithm):
explained_var = explained_variance(self.rollout_buffer.values.flatten(), self.rollout_buffer.returns.flatten())
# Logs
self.logger.record("train/surrogate_loss", np.mean(surrogate_losses))
self.logger.record("train/entropy_loss", np.mean(entropy_losses))
self.logger.record("train/trust_region_loss", np.mean(trust_region_losses))
self.logger.record("train/policy_gradient_loss", np.mean(pg_losses))
self.logger.record("train/value_loss", np.mean(value_losses))
self.logger.record("train/approx_kl", np.mean(approx_kl_divs))